medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
Controlling the first wave of the COVID–19 pandemic in Malawi: results from a panel study
Jethro Banda1 Albert N. Dube1,2 Sarah Brumfield3 Amelia C. Crampin1,4 Georges Reniers4 Abena S. Amoah1,4 Stéphane Helleringer5
ABSTRACT
Many African countries have experienced a first wave of the COVID–19 pandemic between June and August of 2020. According to case counts reported daily by epidemiological surveillance systems, infection rates remained low in most countries. This defied early models of the potential impact of COVID–19 on the continent, that projected large outbreaks and massive strain on health systems. Theories proposed to explain the apparently limited spread of the novel coronavirus in most African countries have emphasized 1) early actions by health authorities (e.g., border closures) and 2) biological or environmental determinants of the transmissibility of SARS-CoV-2 (e.g., warm weather, cross-immunity). In this paper, we explored additional factors that might contribute to the low recorded burden of COVID–19 in Malawi, a low-income country in Southeastern Africa. To do so, we used 4 rounds of panel data collected among a sample of adults during the first 6 months of the pandemic in the country. Our analyses of survey data on SARS-CoV-2 testing and COVID-related symptoms indicate that the size of the outbreak that occurred in June-August 2020 might be larger than recorded by surveillance systems that rely on RT-PCR testing. Our data also document the widespread adoption of physical distancing and mask use in response to the outbreak, whereas most measured patterns of social contacts remained stable during the course of the panel study. These findings will help better project, and respond to, future waves of the pandemic in Malawi and similar settings.
1 Malawi Epidemiological and Intervention Research Unit (Lilongwe and Chilumba, Malawi) 2 University of Malawi College of Medicine (Blantyre, Malawi) 3 Boston University School of Public Health (Boston, United States) 4 London School of Hygiene and Tropical Medicine (London, United Kingdom) 5 New York University – Abu Dhabi (Abu Dhabi, United Arab Emirates)
NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. 1 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
INTRODUCTION
In December 2019, a novel coronavirus, the severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2), emerged in Hubei province, in Central China (1). Since then, SARS-CoV-2 has
been detected in 191 countries. By the end of 2020, over 1.7 million deaths had been attributed
to COVID–19, i.e., the disease caused by SARS-CoV-2 (2). The World Health Organization
(WHO) declared this novel coronavirus a public health emergency of international concern on
30 January 2020 and classified its spread as a global pandemic on 11 March 2020. By
comparison, the transmission of other coronaviruses (e.g., SARS-CoV-1, MERS-CoV) has been
much more limited (3,4).
SARS-CoV-2 has however not spread evenly throughout the world. Europe and the Americas
(e.g., USA, Brazil, Mexico) constitute the most affected regions (5). In these settings, the
recorded incidence of the novel coronavirus has often remained high since the first few months
of 2020, causing significant excess mortality relative to previous years (6–9). Other large
outbreaks have been documented, for example in south Asia (10) as well as in Iran (11). In
contrast, the incidence of SARS-CoV-2 appears low in African countries, which have recorded
fewer than 4% of the global cases and deaths in 2020, despite accounting for more than 15% of
the world population (12).
This limited burden of COVID–19 on the African continent contrasts with expectations and
projections formulated at the beginning of the pandemic. At that time, there were strong
concerns that overcrowding in slum neighborhoods of large cities, limited access to water and
sanitation, inadequate human resources for health, and the high-prevalence of multi-
generational households might create a particularly fertile ground for the spread of SARS-CoV-2
2 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
(13,14). A model from the WHO regional office for Africa thus projected 37 million symptomatic
cases in 2020, requiring high levels of hospitalization and life support throughout the African
continent (15). Whereas some African countries have indeed experienced multiple waves of the
pandemic and high levels of excess mortality (e.g., South Africa), most African countries have
not reported the predicted levels of strain on healthcare systems, and associated increases in
morbidity and mortality (16,17) .
Several theories have been proposed to explain the lower-than-expected burden of COVID–19
in most African countries in 2020. Some theories have emphasized the limited links between the
continent and other world regions where SARS-CoV-2 first spread. African countries might thus
have experienced a delayed onset of SARS-CoV-2 outbreaks due to the low volume of air travel
to the region (18). Urbanization is also more limited in African countries than in other parts of the
world (19). Lower population densities in rural areas limit opportunities for spreading SARS-
CoV-2 in large regions of African countries (20). Other theories have suggested that the
transmissibility of the novel coronavirus might be lower on most of the African continent. This
might be because of climate factors, since SARS-CoV-2 spreads less efficiently in warm
weather (21). It might also be attributed to (partial) protection from infection with SARS-CoV-2
due to prior exposure to other coronaviruses (17,21), genetic differences in susceptibility to
viruses (22), or the non-specific effects of some vaccines (e.g., BCG) frequently administered to
local populations (23). Furthermore, youthful age pyramids in African countries leave a smaller
proportion of the population at-risk of severe COVID–19 and death compared to populations
with an older age structure (20,24,25).
In addition, rapid policy responses might have played a key role in limiting the spread of SARS-
CoV-2 in African countries. For example, several African countries closed their borders when
COVID–19 cases were growing rapidly in China and in Europe. This might have delayed and/or
3 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
reduced the importation of SARS-CoV-2. Restrictions on schools and gatherings and/or
lockdowns might have reduced the scale on which the novel coronavirus could spread during
the early phase of the pandemic. In South Africa, a lockdown and its associated travel
restrictions might have limited the growth of the epidemic between March and June 2020 (26).
Recent experience with other epidemic diseases (e.g., Ebola, HIV) might have better prepared
local health actors for engaging local communities, and implementing required information
campaigns, quarantines and contact tracing (27,28).
Beyond these oft-discussed factors, the limited burden of COVID–19 on the African continent
might also be an artifact of incomplete data collection systems (12). As in other regions of the
world, the recording of cases by epidemiological surveillance systems in African countries
depends on the extent of testing for SARS-CoV-2 through RT-PCR. In several countries, the
laboratory capacity to conduct such tests was not immediately available at the start of the
outbreak and had to be established. In other countries, few reference laboratories had the
capacity to carry out RT-PCR tests. These laboratories might have been initially overwhelmed
by the volume of tests to be conducted to support pandemic response. Subsequently, the
testing capacity has been greatly expanded in several countries, for example by mobilizing
GeneXpert machines commonly used to test for tuberculosis (29). Nonetheless, African
countries have so far conducted fewer tests per 1,000 people than elsewhere (30). The
proportion of COVID–19 cases (and deaths) that are not recorded in case-based surveillance
systems might thus be larger in African countries than in other parts of the world (12). Most
African countries also lack death registration systems that are sufficiently complete to allow
detecting peaks of excess mortality due to the pandemic, even in the absence of extensive
testing for SARS-CoV-2 (31).
4 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
Even though governments were often quick to adopt recommendations from the WHO and the
African CDC for the prevention of SARS-CoV-2 spread in local communities (e.g., physical
distancing, enhanced hand hygiene, use of facial masks), few studies have investigated the
contributions of such behavioral changes to epidemic control in African countries. Surveys
conducted early in the pandemic in African countries, e.g., in the first few weeks after the
introduction of SARS-CoV-2 in a country, have assessed the level of knowledge, and the
perceptions of risk, about COVID-19 among various population groups (32–36). They have
highlighted widespread concerns about getting infected with SARS-CoV-2, gaps in
understanding of the modes of transmission of SARS-CoV-2, as well as varying levels of early
adoption of protective behaviors (e.g., mask use, physical distancing). Subsequent surveys
have focused predominantly on the impact of the COVID-19 pandemic on several
socioeconomic dimensions including, for example, poverty, education, healthcare utilization,
mental health and family planning (37–40).
In this paper, we analyzed data from a panel study conducted in Malawi during the 6 months
that followed the introduction of SARS-CoV-2 in the country. We used these data to 1) evaluate
the potential extent of under-reporting of COVID–19 cases in case-based surveillance systems,
and 2) explore the pace at which protective behaviors (e.g., physical distancing, mask use) were
adopted in response to the spread of SARS-CoV-2.
BEHAVIORAL CHANGE TO CONTROL THE SPREAD OF SARS-COV-2
The potential for a pathogen like SARS-CoV-2 to spread within a population depends on the
interactions between features of the virus and the characteristics and behaviors of its (human)
5 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
hosts. This is summarized in an indicator called the basic reproduction number, �! (41). It is
defined as the number of new infections produced by a single infection in a completely
susceptible population. An epidemic has the potential to expand when �! is greater than 1, and
it contracts when �! is below 1. �! is often expressed as the product of 3 parameters: (1) the
probability that the virus is transmitted when a susceptible individual comes into contact with
someone who is infected (transmissibility, noted �); (2) the average rate at which susceptible
and infected individuals come into contact (noted �̅), and (3) the amount of time infected
individuals remain contagious, i.e., they are able to transmit the virus (noted �). We thus have:
�! = � ∙ �̅ ∙ � (1)
Controlling a viral outbreak then requires reducing one or several of those parameters, so that
�! drops below 1 in a sustained manner.
SARS-CoV-2 can be transmitted before the appearance, or in the absence, of symptoms (42–
44). Because there are limited effective clinical options to reduce the duration of illness in mild
or moderate COVID–19 cases (45), few control strategies target the parameter �. Instead, most
recommended control strategies focus on altering � and/or �̅. Reducing the transmissibility of
SARS-CoV-2 (�) can be achieved through vaccination, or through behavioral changes that limit
exposure to viral particles emitted by infected individuals. This includes, for example, increasing
hand washing to reduce transmission of SARS-CoV-2 through fomites (46,47), sufficient
physical distancing to ensure that susceptible individuals cannot be reached by large infectious
droplets (48), and wearing facial masks to reduce the risk of infection from droplets and other
aerosols (49). Other interventions to reduce the transmissibility of SARS-CoV-2 may emphasize
increasing ventilation of indoor space to limit transmission via aerosols (50).
6 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
Reducing the rate of contact between susceptible and individuals (�),̅ on the other hand,
requires encouraging or mandating that individuals limit their social interactions. Often, these
injunctions target infected or exposed individuals: after a positive COVID–19 test, a presumptive
diagnosis or a potential exposure to SARS-CoV-2, patients are asked to isolate so that they do
not transmit the virus to others in their household, community or networks (51). In some
circumstances, health authorities may also take wider measures to reduce the extent of contact
within a population. They may temporarily prohibit attendance of certain places or events where
individuals socialize and thus possibly transmit SARS-CoV-2. Since the beginning of the
pandemic, many countries have even imposed periods of “lockdown” or “stay-at-home” orders,
during which individuals are only allowed to leave their homes for restricted reasons and at very
limited times.
In African countries, as in other areas of the world, governments and health stakeholders have
promoted a mix of behavioral changes, which aim to reduce � and �̅ simultaneously.
Investigations of the determinants of epidemic dynamics in African countries have however
focused on changes in the rate of contact between infected and susceptible individuals (�̅).
Several mathematical models have projected the effects of lockdown measures on epidemic
trajectories (52,53). Two empirical studies have relied on time-use surveys to document
changes in patterns of contact in African settings (54,55). They used these data to build detailed
age-specific contact matrices. In comparison with pre-pandemic data, they have shown that
lockdowns reduced the extent of social interactions in target populations and prompted a
decline in estimates of the reproduction number of the epidemic. Other mathematical models
have explored the potential impact on epidemic trajectories in African countries of i) self-
isolation of suspected COVID–19 cases, ii) contact tracing, and iii) “shielding” members of the
most at-risk age groups (i.e., elderly people) (20,53,56). Several surveys have documented the
7 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
prevalence of protective behaviors targeting �, i.e., the transmissibility of SARS-CoV-2, in
African countries (32,33,54,57–59). However, they have seldom investigated whether the scale
on which these behaviors are implemented might have changed over time.
COVID–19 IN MALAWI
Malawi is a low-income country located in southeastern Africa, with a population of
approximately 18 million. It had an estimated life expectancy of 63.7 years in 2019 (60). Since
the 1980’s, it is affected by a large HIV epidemic (61,62). In 2015-16, more than 1 in 10 adults
had HIV, and among those only 76.8% were aware of their HIV status (63). Prior to the COVID-
19 pandemic, Malawi had made significant progress towards the achievement of various health-
related goals, particularly those related to HIV treatment (64,65) and to the improvement
childhood survival (66). In recent years, the incidence of non-communicable diseases linked to
unhealthy behaviors has increased in urban and rural areas (67–69).
Initial projections of the potential impact of the COVID–19 pandemic in Malawi suggested that 1
in 5 Malawians might become infected with SARS-CoV-2 in 2020, resulting in more than 60,000
hospitalizations and close to 2,000 deaths (15). Malawi was one of the last countries in the
world to record a case of COVID–19. This occurred on April 2nd 2020, when infection was
confirmed by RT-PCR in a traveler recently returned to the country (Figure 1). Sporadic clusters
of COVID–19 cases were then detected in urban centers for several weeks. In late May,
additional importations of COVID–19 cases occurred among migrants returning primarily from
South Africa, i.e., the African country with the largest documented outbreak. The recorded
incidence of SARS-CoV-2 then increased sharply in Malawi in June and July, before starting to
8 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
decline in August. The daily number of new cases remained very low until the last weeks of
December, when a sharp rebound in incidence was recorded in the country (figure 1). As of the
end of December 2020, Malawi had recorded 6,583 cases of COVID–19, resulting in 189
deaths. The districts with the most confirmed cases were those where the country’s main cities
are located (i.e., Lilongwe, Blantyre).
[FIGURE 1 ABOUT HERE]
The Government of Malawi has adopted a series of measures to address the spread of SARS-
CoV-2. At the end of March, prior to the emergence of the first COVID–19 cases in the country,
the Government declared COVID-19 a “national disaster”, reduced entries into the country, and
implemented COVID–19 screening at border posts and airports that remained open (e.g., to
allow the entrance of essential goods). The Ministry of Health (MoH) and other stakeholders,
initiated information campaigns (e.g., via radio messages, loudspeaker announcements or
SMS) to increase awareness and knowledge of the pandemic among the population. This
included communications about how SARS-CoV-2 spreads, information about possible
symptoms, and what to do when presenting such symptoms. The MoH also started a toll-free
hotline, as well as social media pages, where frequent updates about the epidemic situation in
Malawi, and recommendations, were posted.
Laboratory-based surveillance systems similar to those used throughout the world were
activated in the country (70). The capacity to detect SARS-CoV-2 by RT-PCR was established
at the end of March at the National reference laboratory in Lilongwe, and at teaching and
research institutions in Blantyre, in the southern region of the country (71). Additional laboratory
sites with capacity to test for SARS-CoV-2 were then added throughout the country between
May and July . As of the end of December 2020, Malawi had carried out more than 85,000 tests,
9 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
i.e., slightly more than 4 tests per 1,000 people. In comparison, neighboring countries have
conducted tests on a larger scale (e.g., 9.6 tests per 1,000 in Mozambique, 38.6 tests per 1,000
in Zambia), whereas in high-income countries, testing rates often exceed 200 tests per 1,000
(30).
COVID-19 tests have been carried out in various contexts in Malawi, including screening of
travelers at border posts and diagnostic tests for suspected cases identified in health facilities.
Tests have also been conducted as part of contact tracing (72), i.e., efforts through which health
workers get in touch with people who have come in contact with a confirmed case of COVID–
19, notify them of their exposure, and encourage them to isolate in an attempt to break
transmission chains. Between the beginning of the outbreak and the end of 2020, more than
10,000 contacts of COVID–19 cases have been listed and followed-up in the country.
In Malawi, the MoH and other stakeholders (e.g., organizations of the UN system, Non-
Governmental Organizations) have sought to reduce the transmissibility of the novel
coronavirus using various channels. Protective behaviors such as increased hand washing,
coughing or sneezing in one’s elbow, and physical distancing were thus rapidly promoted. Early
in the outbreak, the MoH also emphasized the use of facial masks to prevent the transmission
of SARS-CoV-2. Medical masks and respirators were recommended in healthcare settings, and
among people who care for patients with COVID-19, whereas the general public was advised to
use cloth masks “in settings where social distancing is not possible and where there is
widespread community transmission” (73). The recommendations were accompanied by
guidelines intended for local tailors about the making of cloth masks (e.g., number of fabric
layers). To respond to the increased spread of SARS-CoV-2 in June and July (see figure 1), the
Government made mask wearing mandatory when in public in early August. The measure was
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accompanied by a fine of 10,000 Malawian Kwachas (i.e., approximately 12 USD) for non-
compliers.
Concurrently, several measures were taken to reduce contact between susceptible and infected
individuals in the population. As part of the national disaster declaration at the end of March,
schools and universities were closed throughout the country, and attendance of public
gatherings was limited to 100 people. A national lockdown was then announced on April 18th
(74). However, several organizations contested the proposed plan. Following legal procedures,
the Malawi High Court suspended the implementation of this “stay-at-home” order. Unlike other
countries in Eastern and Southern Africa (26), Malawi thus never experienced a nationwide
lockdown in 2020. The government of Malawi nonetheless adopted other measures to reduce
contacts in specific populations. For example, some prisoners were released to help reduce
overcrowding, and thus prevent large outbreaks, in prisons in August 2020. In recent months,
some of the restrictions on social contact have been lifted in Malawi. For example, schools and
universities reopened in September. International travel (e.g., flights) also resumed on a larger
scale at that time, before being interrupted again in December in response to increased
incidence of SARS-CoV-2 (figure 1).
DATA AND METHODS
Panel dataset: To evaluate potential explanations for the low observed burden of COVID–19 in
Malawi, we analyzed panel data collected during the first 6 months of the pandemic in the
country (“COVID–19 panel” thereafter). This panel was initiated in April 2020, roughly a month
after the Government of Malawi declared COVID–19 a “national disaster”, and less than 3
11 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
weeks after the first cases of COVID–19 detected in Lilongwe, the capital city. The COVID–19
panel now includes 4 rounds of data collection conducted approximately 6 weeks apart and
ending in mid-November 2020. During that time, the recorded incidence of the SARS-CoV-2
virus varied greatly, with peaks of close to 200 cases per day in July vs. fewer than 5 cases per
day on average in October and November (figure 1). The first two rounds of data collection also
took place during a time when campaigning for the presidential election of June 23rd was under
way (75).
Respondents in the COVID–19 panel had previously participated in a study of the measurement
of adult mortality. This “pre-COVID–19 study” was completed before the introduction of SARS-
CoV-2 in Malawi. It was nested within the activities of a health and demographic surveillance
system (HDSS) located in Karonga district, in northern Malawi (76,77). This is an area bordering
Lake Malawi to the east and located within 1.5-hour drive of the Tanzania border. Its economy is
predominantly organized around fishing, subsistence farming (e.g., rice, cassava) and small-
scale retail activities.
The last round of interviews for the pre-COVID–19 study focused on the health and family
relations of respondents. It occurred between December 2019 and March 2020. At that time, we
sought to obtain the mobile phone numbers of 1) a random sample of current residents of
several HDSS population clusters located close to the lakeshore community of Chilumba
(“current residents”, thereafter), 2) a random sample of former HDSS residents who had
migrated throughout Malawi (“former residents”, thereafter), and 3) the siblings of these current
and former HDSS residents (“referred siblings”, thereafter). When the spread of SARS-CoV-2
accelerated throughout the world (including in other parts of Africa) in March 2020, we decided
to initiate the COVID–19 panel: we contacted all participants of the pre-COVID-19 study for
12 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
whom a phone number was available, and invited them to participate in a series of COVID-
related interviews.
Study sample: In total, we sought to obtain the phone numbers of 1,036 participants in the pre-
COVID–19 study and were successful for 779 participants (75.2%). Among those approximately
80% agreed to enroll in the COVID–19 panel (n = 619). More detailed descriptions of the
constitution of the COVID–19 panel are available elsewhere (78). Compared to other
participants in the pre-COVID–19 study, those who joined the COVID–19 panel were more
educated (78). Follow-up rates from one round of the COVID–19 panel to the next were 93-
95%, and in total, 543 respondents completed all 4 rounds of the COVID–19 panel (figure 2).
[FIGURE 2 ABOUT HERE]
Participants in the COVID–19 panel included men and women aged 18 years and older. The
majority of respondents were residents of rural and peri-urban parts of Karonga district (see
appendix A1). Other participants in the COVID–19 panel were dispersed throughout the country
(appendix A1), including in districts and large urban centers of the central and southern regions.
Due to its sampling frame, and to selective participation in a survey conducted by mobile phone,
the sample of the COVID–19 panel is not representative of the population of Malawi, or Karonga
district. We thus do not use these data to estimate the prevalence of various symptoms or
behaviors in the country at various points during the pandemic. Instead, we use multiple
measurements available for each panel participant to a) understand the potential scale of under-
reporting of COVID–19 in data derived from RT-PCR tests, and b) assess the pace at which
study participants adopted protective behaviors against SARS-CoV-2.
13 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
Data collection: All interviews of the COVID–19 panel were conducted by mobile phone, by a
team of trained interviewers. This team had previously participated in data collection of the most
recent Malawi Demographic and Health Survey (2016), as well as in the pre-COVID–19 study.
Each interviewer was randomly assigned a set of respondents at the beginning of the COVID–
19 panel, and these sets were maintained in all 4 rounds of data collection. As in other surveys
conducted during the COVID–19 pandemic in African countries (e.g., 79), data collectors
conducted interviews from their own homes. We sought oral informed consent from all
participants before each round of data collection. Respondents who completed an interview
were provided with 1,200 Malawian Kwachas worth of mobile phone units (approximately 1.6
US Dollars). Data collection procedures for the COVID–19 panel were approved by the
institutional review boards of the National Health Sciences Research Committee in Malawi, the
Johns Hopkins University School of Public Health and the London School of Hygiene and
Tropical Medicine.
Measuring COVID-related symptoms and behaviors: In each round of the COVID–19 panel,
we asked respondents whether they had experienced, during the previous month, a number of
symptoms previously reported among COVID–19 cases in clinical studies (11,80,81). These
symptoms included, coughing, shortness of breath, chest pain, fever, headaches or fatigue. We
only included loss of smell/taste (82,83) in the list of symptoms elicited in rounds 2, 3 and 4 of
the COVID–19 panel. Starting in round 3, we asked respondents who reported any symptom
possibly related to COVID–19 whether they self-isolated upon experiencing these symptoms.
We also asked respondents whether they had ever been tested for SARS-CoV-2. In round 4, we
added a description of the sample collection procedure in our question about SARS-CoV-2
testing (i.e., that a long swab was inserted in the patient’s nostrils). This description was
introduced to help prevent confusion with other COVID-related screening procedures (e.g.,
temperature checks) widely in Malawi implemented since the start of the pandemic.
14 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
In each round of data collection, we investigated the behaviors respondents used to reduce the
transmissibility of SARS-CoV-2 and/or limit their contacts in the month before the survey. First,
to explore changes in �,̅ i.e., the rate of contact between infected and susceptible individuals,
we inquired about respondents’ living arrangements. We asked respondents how many people
resided in their households, and how many rooms their house included. This provided an
indicator of the potential for intra-household exposure to/transmission of SARS-CoV-2. Second,
to measure contacts outside of the household, we asked respondents whether they had
attended various places and events. We drew a list of places where people commonly interact
and socialize in Malawi, which included markets, churches and mosques, neighbors or relatives’
houses, workplaces, football games or funerals. Then, we asked respondents if they had
attended each of these places/events in the past 7 days before the interview.
We also asked respondents to list the behaviors they had used to reduce the spread of SARS-
CoV-2 in the past month. This question was adapted from instruments previously administered
in COVID-related surveys conducted in high-income countries (84). During pre-testing and
piloting activities, we drew a list of possible answers to this question. This list included behaviors
that reduce �, e.g., use of facial masks, physical distancing or handwashing, as well as
behaviors that reduce �̅, e.g., avoiding crowded areas. Interviewers did not read this list to
respondents. Instead, they let them spontaneously recall their behaviors, and coded their
answer using the list of potential behaviors. If a behavior was not included in the list, they coded
it as “other” and specified the respondent’s answer in a follow-up question. Multiple answers
were allowed, and after each answer, interviewers were instructed to probe further in a non-
specific manner, by asking respondents whether there was anything else that they did to
prevent the spread of SARS-CoV-2.
15 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
In rounds 3 and 4, we asked respondents who reported wearing a facial mask in the past month
how often they wore one during that timeframe. Possible answers were “always”, “often”,
“sometimes” and “rarely”. We also asked them to list the reasons why they wore a mask.
Interviewers let respondents answer spontaneously and they coded their answer using a list of
possible reasons. This list included, for example, “self-protection”, “to protect others”, “to meet a
requirement (e.g., to enter a building)” or “to avoid a fine”. Multiple answers were allowed. After
each reason mentioned by a respondent, interviewers were instructed to probe further by asking
whether there was an additional reason.
Data analysis: we first described the socio-economic characteristics of panel participants who
completed all 4 rounds of data collection, including their reported gender, age, marital status,
occupation and religion. Second, we analyzed trends in their experience of COVID-related
symptoms over time. We classified participants in 3 groups according to their reported
experience of symptoms during the course of the study: those without any reported symptoms,
those who developed a cough, and those who developed one or more other symptoms
previously associated with COVID-19. We then assessed the level of testing for SARS-CoV-2 in
our panel sample, and we explored whether testing rates varied across these 3 groups. To
measure testing rates, we only considered SARS-CoV-2 testing histories reported during round
4, because we introduced a more precisely worded question during that round. Low testing
rates among those with new onset of various COVID-related symptoms constitute an indication
of the extent of under-reporting of the SARS-CoV-2 burden in this sample.
Third, we described trends in behaviors associated with social contacts in our panel sample. To
assess the potential for intra-household contact, we reported the ratio of the number of
household residents to the number of rooms in the house. Then, we tabulated reports of
attendance of places and events in the past week collected during each round. We measured
16 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
the proportion of respondents reporting that they self-isolated when they experienced symptoms
indicative of COVID–19, and we tested whether the likelihood of self-isolation varied depending
on the type of symptom experienced. We tabulated other reported behaviors, which might
reduce the rate of contact between infected and susceptible individuals, by data collection
round.
Finally, we measured trends in reported behaviors related to the transmissibility of the virus (�)
across all 4 rounds. We included all such behaviors listed in response to our open-ended
question about behaviors used to prevent SARS-CoV-2 spread. For respondents who reported
using facial masks in rounds 3 & 4, we also measured the reported frequency of mask use, and
we described the reasons they listed for using facial masks.
Data compiled by the MoH suggest that SARS-CoV-2 spread more extensively in large cities in
Malawi. We thus conducted all analyses of the panel dataset separately for residents residing in
urban vs. rural areas. We used the definition of “urban” areas from the Malawi National
Statistical Office: “urban centers in Malawi refer to the four major cities [of] Blantyre, Lilongwe,
Mzuzu, and Zomba, town councils and bomas6 and other town-planning areas” (85). All other
areas of residence were classified as “rural”. All analyses were conducted using the statistical
software, R.
6 Administrative capitals of each district
17 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
RESULTS
Descriptive statistics: There were 543 participants who completed the four rounds of the
COVID–19 panel (figure 2). Among those, 419 resided in rural areas at round 1, whereas 124
resided in urban areas (table 1). In this sample, there were few respondents aged 55 and older
(approximately 2%), whereas close to 1 in 5 full participants was aged 18–24 years old. There
were large differences in the marital status, water source, occupation and religion of residents
residing in rural vs. urban areas. Rural participants were more likely to be currently married
(64.0%) or formerly married (21.9%) than urban participants (54.8% and 10.4%, respectively).
Rural participants relied more frequently on boreholes as their primary water source (55.1% vs.
4.8% in urban areas) and they engaged in activities related to agriculture and fishing at a much
higher rate (46.0% vs. 8.6% in urban areas).
[TABLE 1 ABOUT HERE]
Symptoms and SARS-CoV-2 testing: The proportion of respondents reporting various
symptoms associated with COVID–19 varied during the course of the panel study. The
proportion of rural participants reporting a cough in the month prior to the survey thus declined
steadily from 28.2% in round 1 to 18.1% in round 4 (figure 3). Among urban participants, on the
other hand, the proportion of respondents reporting a cough in the previous almost doubled
between rounds 1 & 2 (from 17.7% to 30.5%), before returning to lower levels in rounds 3 and 4
(16.0% and 17.8%, respectively). Other symptoms associated with COVID–19 displayed similar
trends among respondents in urban areas. For example, no urban participants reported chest
pain in round 1 of the panel vs 5% in round 2, and <2% in rounds 3 and 4 (not shown).
18 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
[FIGURE 3 ABOUT HERE]
Few participants (5.9%) reported being tested for SARS-CoV-2 since the start of the pandemic
in Malawi. Among rural participants, the likelihood of SARS-CoV-2 testing was not associated
with experience of symptoms related to COVID–19 (figure 4), whereas among urban
participants, the likelihood of SARS-CoV-2 testing was slightly higher among those who had
developed a cough or other symptoms associated with COVID–19 during the course of the
study. Rural participants reported being tested at health facilities or at home (as part of contact
tracing efforts), whereas a few urban participants also reported being tested at border posts.
[FIGURE 4 ABOUT HERE]
Changes in contact patterns (� ): there were limited changes in the living arrangements of
panel participants during the course of the study (figure 5). Both rural and urban participants
reported living in households with a median density of approximately 2 people per room.
Crowded households (i.e., with more than 3 people per room) were slightly more common in
rural areas. In addition, <1% of respondents reported using residential strategies (e.g., moving
to a new house or flat, appendix A2) to reduce the spread of SARS-CoV-2.
[FIGURE 5 ABOUT HERE]
There were limited changes in attendance of places and events where people socialize across
all 4 rounds. Rural and urban participants reported most commonly visiting markets, their
neighbors’ or relatives’ homes, places of worship and workplaces (figure 6). The proportion of
rural participants reporting attending a funeral in the past 7 days declined slightly after round 1
19 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
of the panel, from 47.5% in round 1 to approximately 37% in rounds 2, 3 and 4. The proportion
of rural participants attending football games varied between rounds, from 17.9% in round 1 to
38.3% in round 2. On the other hand, attendance of schools/universities increased sharply
between rounds 3 and 4: among participants in rural areas, the proportion of participants visiting
a school in the past 7 days increased from 3.6% in round 1 to 13.8% in round 4; among urban
residents, similar figures were 3.2% and 27.9%. Very few participants reported attending
weddings and baptisms in the early rounds of the panel. However, the proportion of urban
residents who reported attending a wedding in the past week increased from <1% in round 1 to
17.8% in round 4.
[FIGURE 6 ABOUT HERE]
Compliance with isolation guidelines was limited among panel participants (figure 7). The
proportion of participants experiencing COVID-related symptoms who reported self-isolating
was 29.1% in round 3 vs. 24.5% in round 4. The likelihood of self-isolation also varied by a)
place of residence, and b) the type of COVID-related symptom experienced. In particular,
participants who experienced a cough were more likely to report self-isolating than participants
who reported other symptoms (e.g., fever, headaches). Self-isolation was especially high during
round 3 among urban participants who had recently been coughing (i.e., 70%).
[FIGURE 7 ABOUT HERE]
Some panel participants also reported “avoiding crowded areas” or “avoiding going out in
general” to reduce the spread of SARS-CoV-2 (appendix A2). However, the proportion of
respondents using these strategies to reduce contact declined after round 1. Very few
respondents also reported reducing their mobility to prevent the spread of SARS-CoV-2
20 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
(appendix A2). In round 1, >10% of panel participants in urban areas reported “avoiding visibly
sick people” to prevent the spread of SARS-CoV-2, but in subsequent rounds, this proportion
declined to <2%. Virtually none of the panel participants reported “avoiding healthcare facilities”
to reduce their risk of acquiring or transmitting SARS-CoV-2.
Changes in behaviors related to transmissibility (�): panel participants reported adopting
various behaviors that modify the transmissibility of SARS-CoV-2. In each round, more than
90% of participants reported washing their hands more often (figure 8). In round 1, the next
most commonly reported behaviors affecting transmissibility were the use of hand sanitizer
among urban respondents and covering up coughs/sneezes among rural residents. In
subsequent rounds however, panel participants reported adopting other strategies as their main
preventive behaviors. For example, whereas the use of physical distancing was reported by
approximately 10% of participants in round 1, it increased to close to 60% in round 2 among
participants in urban areas, and to similar levels in round 3 among participants in rural areas.
The use of physical distancing then stopped increasing in rural areas in round 4, and even
declined among urban participants.
[FIGURE 8 ABOUT HERE]
In all rounds, the use of facial masks in the month prior to the survey was more widespread
among participants in urban areas than in rural areas (figure 8). Reported mask use did not
increase markedly between rounds 1 (4.4%) and 2 (7.1%) among participants in rural areas,
whereas the proportion of urban participants who reported using a mask almost doubled during
the same timeframe (20.3% in round 1 vs. 38.3% in round 2). The proportions of participants
reporting use of facial masks in the month prior to the survey then increased particularly sharply
21 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
in subsequent rounds: in round 3, 89.6% of participants in urban areas, and 67.7% in rural
areas, reported using a mask in the past month.
The proportion of participants who reported using a mask at any point in the past month
remained approximately constant in rounds 3 & 4 of the panel study. However, the frequency of
mask use declined markedly between round 3 and 4 (figure 9). In rural areas, the proportions of
mask users who reported wearing masks either “always” or “very often” declined from 71.2% to
39.7%, whereas in urban areas, corresponding figures were 78.9% and 44.7%.
The reasons for wearing masks reported by participants also varied between rounds 3 and 4. In
round 3, self-protection and protection of others were the most commonly reported reasons for
mask use. In round 4, on the other hand, more than 50% of participants also reported wearing a
mask due to requirements, either to enter a building or to receive a service (appendix A3). In
rural areas, the proportion of mask users who reported wearing a mask to comply with a
requirement nearly doubled between rounds 3 and 4. Fewer than 1% of respondents reported
wearing a mask because they worried about fines imposed by the Government, because of
social pressure or because they were sick or presented symptoms.
DISCUSSION
In this study, we found that the magnitude of the SARS-CoV-2 outbreak in the country is likely
larger than reflected in case-based surveillance datasets that rely on RT-PCR diagnostics.
When we compared participants’ self-reports of a) symptoms often associated with COVID–19
(e.g., cough, chest pain) and b) their history of RT-PCR testing for SARS-CoV-2, we found that
22 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
testing rates were low (approximately 5% of study participants). This was the case even among
those who reported experiencing symptoms commonly associated with COVID–19 in clinical
studies (e.g., cough, chest pain). As a result of very limited SARS-CoV-2 testing coverage, a
number of cases might have remained undetected and were thus not included in daily tallies of
cases compiled by the MoH.
This finding about the completeness of case counts derived from RT-PCR testing is affected by
potential limitations. Our self-reported measure of prior SARS-CoV-2 testing, in particular, might
misrepresent testing coverage. Due to stigma surrounding COVID–19 (86), some respondents
might have failed to report previous tests for SARS-CoV-2. This would have prompted a
downward bias in our estimates of testing coverage. Despite including a description of the
sample collection procedure in our survey question in round 4, some participants might have
confused SARS-CoV-2 testing with other prevention measures, e.g., temperature checks,
screening questionnaires. This would have led to bias in the opposite direction, possibly inflating
estimates of the proportion of participants tested for SARS-CoV-2. We also did not investigate
repeat testing for SARS-CoV-2 among panel participants, nor did we assess whether testing
coverage varied over time. Based on these data, and also owing to the selectivity of our study
sample (see below), we could not produce an estimate of the true number of infections with
SARS-CoV-2 in Malawi. Nonetheless, our findings corroborate results from a serosurvey
conducted in Blantyre (87) and several similar surveys conducted in other African settings
(12,88,89). In these studies, the high prevalence of IgG antibodies against SARS-CoV-2 implied
a number of cases (much) larger than recorded by surveillance systems relying on RT-PCR
testing.
Whereas our data indicated that daily case counts based on RT-PCR diagnostics might
underestimate the scale of the outbreak, they also suggested that such laboratory-based
23 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
surveillance systems might adequately capture epidemic trajectories. In particular, the panel
data we collected on symptoms associated with COVID–19 indicated that an epidemic peak
likely occurred in urban centers in June/July (figure 3), similar to trends detected in daily reports
of PCR-confirmed cases (figure 1). At that time, the proportion of urban participants who
reported symptoms commonly seen among COVID–19 cases (e.g., cough, chest pain) nearly
doubled relative to levels observed in earlier months (i.e., round 1 in April/May). In subsequent
rounds (i.e., rounds 3 and 4), this proportion rapidly returned to ‘”normal” levels. In contrast, the
proportion of panel participants residing in rural areas and who reported a cough or another
symptom, declined steadily from round to round, consistent with seasonal patterns of respiratory
infections (90).
We found that several protective behaviors were adopted widely in our study sample during the
first few months of the COVID-19 pandemic in Malawi. These included in particular behaviors
that reduce the transmissibility of the novel coronavirus. More than half of panel participants
reported adopting the recommended practice of physical distancing between rounds 1 & 3.
Mask use also increased very sharply during that time frame, with more than 8 out of 10 urban
participants reporting that they had worn a facial mask outside of their household in the past
month (figure 8). Comparatively, behaviors that affect contacts between infected and
susceptible individuals were more stable in our panel sample. Indicators of intra-household
contact remained largely constant throughout the 4 panel rounds, and there were few changes
in attendance of places and events where people socialize (and thus possibly spread SARS-
CoV-2) during the course of the study
Our data on behavioral responses to the COVID–19 pandemic also indicate that panel
participants have recently reduced their protective behaviors. For example, urban respondents
are increasingly celebrating weddings (figure 6), a social occasion that had led to large SARS-
24 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
CoV-2 clusters in other contexts (91). Most respondents have also reduced the consistency of
their mask use: whereas in round 3, more than two thirds of mask users reported “always”
wearing a mask outside of their household, this proportion dropped to <25% in round 4. Mask
use appears increasingly sustained by requirements to wear masks to access various buildings
and services, rather than by a motivation to protect others. These trends might enhance the
likelihood of local transmission of SARS-CoV-2 in the event of new imported cases and/or the
emergence of more transmissible viral strains (92).
These findings about the adoption of protective behaviors suffer from several limitations. First,
respondents might have over-reported their use of recommended protective measures. For
example, a recent study found that, in a sub-county of Kenya, self-reported mask use greatly
exceeded the levels of mask use recorded by independent observers (93). In our study, the
extent of such social desirability bias in self-reports of protective behaviors might have varied
over time: incentives to report compliance with mask use recommendations might have been
higher after the MoH had mandated the use of masks in public and imposed fines on non-
compliers. Since this mandate was adopted when confirmed cases were near their peak (i.e.,
early August), this might have created a spurious temporal association between increasing
reported mask use and declining case/symptoms. In our data however, the reported frequency
of mask use declined sharply in round 4, at a time when the mandate to wear masks in public
was still in place.
Second, our data on patterns of contact also lacked a proper baseline. Our first panel
measurement took place in April/May, i.e., a few weeks after the Government declared COVID–
19 a “national disaster”, closed schools and universities, and imposed limitations on the size of
public gatherings. Reductions in attendance of social events (e.g., weddings) have likely
occurred before our first data collection round. Such reductions in contact might have helped
25 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
delay the start of the outbreak and reduce its magnitude. However, they did not fully prevent the
local transmission of SARS-CoV-2: incidence increased sharply in June/July when restrictions
on public gatherings were still in place. Our data on self-isolation are also limited because they
was only collected in rounds 3 and 4. They indicated limited compliance with self-isolation
guidelines, except among residents of urban areas who presented with a cough at the peak of
the outbreak (figure 7). This factor might have contributed to slowing down the first wave of the
pandemic in the country.
Third, there are several sampling limitations. Despite high enrollment and follow-up rates (figure
2), the sample size available for panel analyses was limited. We thus could not investigate the
correlates of observed trends in symptoms and behaviors (e.g., gender differences, differences
by occupation). Because it relied on mobile phones to conduct interviews, our sample was also
selective. We thus reached more educated potential participants at a higher rate than potential
participants with no schooling, or only primary education (78). If patterns of behavioral changes
differed by educational levels, then our study results might be affected. Furthermore, the study
sample was not representative of the population of Malawi, Karonga district or the urban areas
of the country. It included members of families with ties to the area surrounding the lakeshore
community of Chilumba and covered by the Karonga HDSS. These families were initially
selected at random among household rosters compiled by the HDSS, during the pre-COVID–19
study. Their members are now dispersed throughout Malawi, including in the large cities most
affected by the first wave of the COVID–19 pandemic in the country.
Finally, our analyses of behavioral changes can only suggest temporal associations with
epidemic outcomes. In our data, reductions in the recorded incidence of SARS-CoV-2 (figure 1)
and in self-reports of COVID–19 symptoms (figure 3) were preceded by, or occurred at
approximately the same time as, the adoption of several protective behaviors (e.g., physical
26 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
distancing, mask use). These apparent associations cannot be interpreted as causa, because
we cannot rule out several competing explanations for this reversal of epidemic trends, due to
data limitations. For example, the decline in incidence/symptoms of SARS-CoV-2/COVID–19
might have been prompted by successes in contact tracing, or changes in biological factors
affecting the transmissibility of the novel coronavirus (e.g., temperature, humidity). Future
investigations of the determinants of SARS-CoV-2 trends in Malawi and other African countries
should triangulate multiple sources of data (e.g., behavioral interviews, viral sequences,
serosurveys, contact tracing datasets).
Despite these limitations, our study adds to our understanding of the initial wave of the COVID–
19 pandemic in Malawi. In highlighting that the number of persons infected during the first wave
of the pandemic might be higher than recorded in surveillance systems that rely on RT-PCR
testing, our work might help estimate the number of people who are still susceptible to the novel
coronavirus more accurately. This might help strengthen projections of the impact of future
waves of the COVID–19 pandemic in the country. Our results also emphasize the potential
importance of population-level behavioral changes in containing SARS-CoV-2 outbreaks in
Malawi. Highlighting the role that physical distancing and mask use might have played in
containing the first COVID–19 wave might help sustain the implementation of these strategies in
subsequent pandemic waves.
27 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
200
7−day moving average
150
100
50 Daily number of confirmedDaily number cases
0
April June August October December
FIGURE 1: RECORDED CASES IN MALAWI
Notes: Data plotted in this graph were drawn from daily reports from the Public Health Institute of Malawi. The red line represents the 7-day moving average (calculated using the zoo package in R).
28 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
1,036 participants in pre- COVID–19 study asked to provide phone number
No phone number available (n=257)
779 sampled participants Wrong number (n=22) Number not reached (n=107) Refused (n=4) Younger than 18 (n=11) Number unavailable (n=10) Died (n=1) Moved abroad (n=2) Wrong respondents (n=3)
619 completed round 1
Number not reached (n=35) Moved abroad (n=1) Refused (n=2)
581 completed first 2 rounds
Number not reached (n=13) Refused (n=1)
567 completed first 3 rounds
Number not reached (n=24)
543 completed all 4 rounds
FIGURE 2: FLOW CHART OF STUDY PARTICIPATION
29 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
Area of Residence Rural Urban Sex of the respondent Male 169 (40.3%) 48 (38.7%) Female 250 (5 9.7%) 76 (61.3%) Age 18-24 76 (18.1%) 25 (20.2%) 25-34 144 (34.4%) 42 (33.9%) 35-44 133 (31.7%) 41 (33.1%) 45-54 55 (13.1%) 14 (11.3%) ≥55 11 (2 .6%) 2 (1.6%) Marital status Currently married 268 (64.0%) 68 (54.8%) Separated 55 (13.1%) 5 (4.0%) Divorced 21 (5.0%) 4 (3.2%) Widowed 16 (3.8%) 4 (3.2%) Never married 59 (14 .1%) 43 (34.7%) Water source Piped water in house 74 (17.7%) 69 (55.6%) Piped water - shared 65 (15.5%) 44 (35.5%) Borehole 231 (55.1%) 6 (4.8%) Lake/River 49 (11.7%) 5 (4.0%) Occupation1 Professional/technical/managerial 15 (3.6%) 29 (22.7%) Sales and services 102 (24.6%) 37 (28.9%) Manual work 57 (13.8%) 24 (18.8%) Domestic service 50 (12.0%) 27 (21.1%) Agriculture/fishing 191 (46.0%) 11 (8.6%) Religion2 Catholic 73 (17.5%) 26 (20.8%) CCAP3 81 (19.4%) 41 (32.8%) Pentecostal 83 (19.9%) 28 (22.4%) Other Traditional churches 140 (33.5%) 15 (12.0%) Other 41 (9.8%) 15 (12.0%)
TABLE 1: CHARACTERISTICS OF PANEL PARTICIPANTS REPORTED DURING ROUND 1 OF DATA COLLECTION, URBAN VS RURAL Notes: all figures in parentheses are column percentages; 1 Occupation was measured during round 2 of the COVID–19 panel. 2 Religion was measured during round 3 of the COVID–19 panel. 3 CCAP = Church of Central Africa Presbyterian.
30 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
0.4
0.3
0.2
0.1 Proportion of respondents reporting a cough
0.0
Rural Urban Residence
Rd 1 (Apr−May) Rd 2 (Jun−Jul) Rd 3 (Aug−Sep) Rd 4 (Oct−Nov)
FIGURE 3: PREVALENCE OF REPORTED COUGH, URBAN VS RURAL Notes: the survey question asked respondents whether they experienced a cough in the past month prior to the survey. Estimates of the standard errors (used to calculate confidence intervals) were adjusted for the clustering of observations within families.
31 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
Rural Urban
1.00
0.75
0.50 Proportion of Respondents
0.25
0.00
None Cough Other symptoms None Cough Other symptoms COVID−related symptoms
Never tested Tested Don't know
FIGURE 4: COVID-19 TESTING ACCORDING TO EXPERIENCE OF COVID-RELATED SYMPTOMS Notes: The experience of symptoms was ascertained over the course of the panel study, whereas the experience of testing for SARS-CoV-2 was reported in round 4. COVID-related symptoms elicited during the surveys included fever, chest pains, shortness of breath, fatigue, shivering, headache, muscle pain, runny nose, sore throat, cough and wheezing.
32 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
Rural Urban
● 10.0
●
●
● ● ● ●
● ● ● ●
●
3.0
1.0 Number of HH residents per room
● ●
●
● ● ● ● ● ● ●
● ● ●
● ● ● ● ● ● 0.3
● ● ● ●
1 2 3 4 1 2 3 4 Data collection round
FIGURE 5: CHANGES IN HOUSEHOLD OCCUPANCY RATIO Notes: the y-axis is plotted on a logarithmic scale. The results presented in this graph are derived from two survey questions asking respondents 1) how many people reside in their household, and 2) how many rooms there are in their house. In each round, the median number of residents per room was 2, both in rural and urban places.
33 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
Rural Urban
A market 1432 43 21
A neighbor's home 3412 1423
A church or mosque 1342 1432
A workplace 4321 1234
A relative's home 1 342 4321
A clinic or health facility 2413 4 32 1
A funeral 342 1 4213
A restaurant or bar 3241 2413
A football game 1 43 2 1234
A school or University 321 4 231 4
A traditional Healer 1423 2431
A wedding 2431 12 3 4
A baptism 3421 1234
0 25 50 75 100 0 25 50 75 100 % attending/visiting in the past 7 days
FIGURE 6: ATTENDANCE OF PLACES & EVENTS Notes: the data plotted in this graph were collected from a series of questions asking respondents whether they had attended the places listed on the y-axis in the past 7 days prior to the survey. The point estimates are labeled by their round number on the plot. The places that appear on the y-axis are ordered according to their attendance in urban areas in round 1.
34 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
Rural Urban
1.00
Other symptoms Cough
0.75
0.50
0.25 Proportion of respondents reporting self − isolating
0.00
Rd 3 Rd 4 Rd 3 Rd 4 Round of data collection
FIGURE 7: SELF-ISOLATION BEHAVIORS, BY EXPERIENCE OF SYMPTOMS Notes: the question about self-isolation was only applicable for respondents who reported experiencing one or more COVID-related symptoms in the month prior to the survey. This question was introduced in round 3 of the panel survey. In round 3, the sample size for these analyses was n = 206. In round 4, the sample size was n = 192. Estimates of the standard errors (used to calculate confidence intervals) were adjusted for the clustering of observations within families.
35 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
Rural Urban
Washed hand more often 2431 2341
Used hand sanitizer 2431 1423
Wore face mask 12 43 1 2 43
Covered mouth/nose when coughing 43 21 3421
Practiced physical distancing 1 2 43 1 4 23
Avoided hand shaking 1234 1 234
Enhanced household hygiene 431 2 4312
Wore gloves 2431 2431
Avoided touching face 1342 1423
0 25 50 75 100 0 25 50 75 100 % reporting behavior
FIGURE 8: ADOPTION OF BEHAVIORS TO REDUCE TRANSMISSIBILITY OF SARS-COV-2 Notes: the data plotted in this graph were collected from a question asking respondents what they had done in the past month to prevent the spread of SARS-CoV-2. Respondents were not provided with potential answers. However, after each response, interviewers were instructed to probe further, by asking respondents: “is there anything else that you have done?”. In this graph, the behaviors listed on the y-axis are those that reduce the transmissibility of the coronavirus. Respondents also listed other behaviors, that reduce the contact between infected and susceptible individuals (appendix A3). In each round, <1% of panel respondents reported not doing anything to prevent the spread of SARS-CoV-2. The point estimates are labeled by their round number on the plot. The behaviors that appear on the y-axis are ordered according to their prevalence in urban areas in round 1.
36 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
Rural Urban
1.00
0.75
0.50
0.25 Proportion of Respondents who reported mask in past month using a face 0.00
Rd3 Rd4 Rd3 Rd4 Data collection round
Always Often Sometimes Rarely
FIGURE 9: FREQUENCY OF MASK USAGE Notes: in rounds 3 & 4, we asked respondents who reported that they had used a mask to prevent the spread of SARS-CoV-2, how often they had done so. These data were not collected in rounds 1 & 2. In round 3, the sample size for these analyses was n = 498. In round 4, the sample size was n = 496.
37 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
Karonga
No respondent
1−9 respondents 10°S 10−49 respondents
...50 respondents
12°S
Mzuzu lat
14°S
Lilongwe
16°S
Blantyre
200 km
33°E 34°E 35°E 36°E 37°E 38°E long
APPENDIX A1: GEOGRAPHIC DISTRIBUTION OF RESPONDENTS Notes: reported district of residence of study respondents during round 1. In our survey answer categories, Mzimba district was not split as Mzimba north vs. Mzimba south, as a result, we present data for these two districts jointly on the map. The city corporations of Lilongwe, Mzuzu, Zomba and Blantyre are displayed separately.
38 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
Rural Urban
Avoided crowded areas 243 1 324 1
Avoided going out in general 21 43 3241
Avoided contact with visibly sick people 3241 324 1
Avoided travel within Malawi 4231 4231
Avoided markets 2314 2314
Avoided travel abroad 2341 2341
Moved to new house or flat 1234 1234
Avoided going to clinic or health facility 1324 1234
0 25 50 75 100 0 25 50 75 100 % reporting behavior to prevent SARS−CoV−2 spread in past month
APPENDIX A2: ADDITIONAL REPORTED BEHAVIORS TO LIMIT CONTACT BETWEEN INFECTED AND SUSCEPTIBLE INDIVIDUALS Notes: the data plotted in this graph were collected from a question asking respondents what they had done in the past month to prevent the spread of SARS-CoV-2. Respondents were not provided with potential answers. However, after each response, interviewers were instructed to probe further, by asking respondents: “is there anything else that you have done?”. In this graph, the behaviors listed on the y-axis are those that reduce contact between infected and susceptible individuals. In each round, <1% of panel respondents reported not doing anything to prevent the spread of SARS-CoV-2. The point estimates are labeled by their round number on the plot. The behaviors that appear on the y-axis are listed according to their prevalence in urban areas in round 1.
39 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
Rural Urban
To protect self 4 3 43
To protect others 43 4 3
Requirement (e.g, government) 3 4 3 4
Worried about fine 43 43
Others wear mask 34 34
Sick/showing symptoms 43 34
Fashion−related 34 34
0 25 50 75 100 0 25 50 75 100 % reporting reason for using facial mask
APPENDIX A3: REPORTED REASONS FOR USING A MASK IN THE PAST MONTH Notes: the data plotted in this graph were collected from a question asking respondents who reported having used a mask in the past month, why they had done so. Respondents were not provided with potential answers. However, after each response, interviewers were instructed to probe further, by asking respondents: “was there another reason?”. The point estimates are labeled by their round number on the plot. The reasons that appear on the y-axis are listed according to their prevalence in urban areas in round 1.
40 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
REFERENCES
1. Velavan TP, Meyer CG. The COVID-19 epidemic. Trop Med Int Health. 2020 Mar;25(3):278– 80.
2. Whitworth J. COVID-19: a fast evolving pandemic. Trans R Soc Trop Med Hyg. 2020 Apr;114(4):241–8.
3. Feng D, Vlas SJD, Fang L-Q, Han X-N, Zhao W-J, Sheng S, et al. The SARS epidemic in mainland China: bringing together all epidemiological data. Tropical Medicine & International Health. 2009;14(s1):4–13.
4. Breban R, Riou J, Fontanet A. Interhuman transmissibility of Middle East respiratory syndrome coronavirus: estimation of pandemic risk. The Lancet. 2013 Aug 24;382(9893):694–9.
5. Weekly epidemiological update - 29 December 2020 [Internet]. [cited 2021 Feb 11]. Available from: https://www.who.int/publications/m/item/weekly-epidemiological-update- --29-december-2020
6. Sinnathamby MA, Whitaker H, Coughlan L, Bernal JL, Ramsay M, Andrews N. All-cause excess mortality observed by age group and regions in the first wave of the COVID-19 pandemic in England. Eurosurveillance. 2020 Jul 16;25(28):2001239.
7. Weinberger DM, Chen J, Cohen T, Crawford FW, Mostashari F, Olson D, et al. Estimation of Excess Deaths Associated With the COVID-19 Pandemic in the United States, March to May 2020. JAMA Intern Med [Internet]. 2020 Jul 1 [cited 2020 Jul 5]; Available from: https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2767980
8. Trias-Llimós S, Riffe T, Bilal U. Monitoring life expectancy levels during the COVID-19 pandemic: Example of the unequal impact of the first wave on Spanish regions. PLOS ONE. 2020 Nov 5;15(11):e0241952.
9. Lima E, Vilela E, Peralta A, Rocha MG, Queiroz BL, Gonzåga MR, et al. Exploring excess of deaths in the context of covid pandemic in selected countries of Latin America [Internet]. OSF Preprints; 2020 Jun [cited 2020 Dec 10]. Available from: https://osf.io/xhkp4/
10. Laxminarayan R, Wahl B, Dudala SR, Gopal K, B CM, Neelima S, et al. Epidemiology and transmission dynamics of COVID-19 in two Indian states. Science. 2020 Nov 6;370(6517):691–7.
11. Nikpouraghdam M, Jalali Farahani A, Alishiri G, Heydari S, Ebrahimnia M, Samadinia H, et al. Epidemiological characteristics of coronavirus disease 2019 (COVID-19) patients in IRAN: A single center study. J Clin Virol. 2020;127:104378.
41 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
12. Maeda JM, Nkengasong JN. The puzzle of the COVID-19 pandemic in Africa. Science. 2021 Jan 1;371(6524):27–8.
13. El-Sadr WM, Justman J. Africa in the Path of Covid-19. New England Journal of Medicine. 2020 Apr 17;0(0):null.
14. Nkengasong JN, Mankoula W. Looming threat of COVID-19 infection in Africa: act collectively, and fast. The Lancet. 2020 Mar 14;395(10227):841–2.
15. Cabore JW, Karamagi HC, Kipruto H, Asamani JA, Droti B, Seydi ABW, et al. The potential effects of widespread community transmission of SARS-CoV-2 infection in the World Health Organization African Region: a predictive model. BMJ Global Health. 2020 May 1;5(5):e002647.
16. Wadvalla B-A. How Africa has tackled covid-19. BMJ. 2020 Jul 16;370:m2830.
17. Mbow M, Lell B, Jochems SP, Cisse B, Mboup S, Dewals BG, et al. COVID-19 in Africa: Dampening the storm? Science. 2020 Aug 7;369(6504):624–6.
18. Gilbert M, Pullano G, Pinotti F, Valdano E, Poletto C, Boëlle P-Y, et al. Preparedness and vulnerability of African countries against importations of COVID-19: a modelling study. The Lancet. 2020 Mar 14;395(10227):871–7.
19. Sulemana I, Nketiah-Amponsah E, Codjoe EA, Andoh JAN. Urbanization and income inequality in Sub-Saharan Africa. Sustainable Cities and Society. 2019 Jul 1;48:101544.
20. Diop BZ, Ngom M, Biyong CP, Biyong JNP. The relatively young and rural population may limit the spread and severity of COVID-19 in Africa: a modelling study. BMJ Global Health. 2020 May 1;5(5):e002699.
21. Njenga MK, Dawa J, Nanyingi M, Gachohi J, Ngere I, Letko M, et al. Why is There Low Morbidity and Mortality of COVID-19 in Africa? The American Journal of Tropical Medicine and Hygiene. 2020 Aug 5;103(2):564–9.
22. Quach H, Rotival M, Pothlichet J, Loh Y-HE, Dannemann M, Zidane N, et al. Genetic Adaptation and Neandertal Admixture Shaped the Immune System of Human Populations. Cell. 2016 Oct 20;167(3):643-656.e17.
23. Curtis N, Sparrow A, Ghebreyesus TA, Netea MG. Considering BCG vaccination to reduce the impact of COVID-19. The Lancet. 2020 May 16;395(10236):1545–6.
24. Dowd JB, Andriano L, Brazel DM, Rotondi V, Block P, Ding X, et al. Demographic science aids in understanding the spread and fatality rates of COVID-19. PNAS. 2020 May 5;117(18):9696–8.
42 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
25. Nepomuceno MR, Acosta E, Alburez-Gutierrez D, Aburto JM, Gagnon A, Turra CM. Besides population age structure, health and other demographic factors can contribute to understanding the COVID-19 burden. PNAS. 2020 Jun 23;117(25):13881–3.
26. Carlitz RD, Makhura MN. Life under lockdown: Illustrating tradeoffs in South Africa’s response to COVID-19. World Development. 2021 Jan 1;137:105168.
27. Bavinger JC, Shantha JG, Yeh S. Ebola, COVID-19, and emerging infectious disease: lessons learned and future preparedness. Current Opinion in Ophthalmology. 2020 Sep;31(5):416– 422.
28. Celum C, Barnabas R, Cohen MS, Collier A, El-Sadr W, Holmes KK, et al. Covid-19, Ebola, and HIV — Leveraging Lessons to Maximize Impact. New England Journal of Medicine. 2020 Nov 5;383(19):e106.
29. Rakotosamimanana N, Randrianirina F, Randremanana R, Raherison MS, Rasolofo V, Solofomalala GD, et al. GeneXpert for the diagnosis of COVID-19 in LMICs. The Lancet Global Health. 2020 Dec 1;8(12):e1457–8.
30. Hasell J, Mathieu E, Beltekian D, Macdonald B, Giattino C, Ortiz-Ospina E, et al. A cross- country database of COVID-19 testing. Sci Data. 2020 08;7(1):345.
31. Setel PW, Macfarlane SB, Szreter S, Mikkelsen L, Jha P, Stout S, et al. A scandal of invisibility: making everyone count by counting everyone. Lancet. 2007 Nov 3;370(9598):1569–77.
32. Iloanusi N-JR, Iloanusi S, Mgbere O, Ajayi A, Essien EJ. COVID-19 Related Knowledge, Attitude and Practices in a Southeastern City in Nigeria: A Cross-Sectional Survey [Internet]. Rochester, NY: Social Science Research Network; 2020 Sep [cited 2020 Dec 9]. Report No.: ID 3683766. Available from: https://papers.ssrn.com/abstract=3683766
33. Reuben RC, Danladi MMA, Saleh DA, Ejembi PE. Knowledge, Attitudes and Practices Towards COVID-19: An Epidemiological Survey in North-Central Nigeria. J Community Health [Internet]. 2020 Jul 7 [cited 2020 Dec 9]; Available from: https://doi.org/10.1007/s10900-020-00881-1
34. Austrian K, Pinchoff J, Tidwell JB, White C, Abuya T, Kangwana B, et al. COVID-19 related knowledge, attitudes, practices and needs of households in informal settlements in Nairobi, Kenya. :21.
35. Kassa AM, Mekonen AM, Yesuf KA, Tadesse AW, Bogale GG. Knowledge level and factors influencing prevention of COVID-19 pandemic among residents of Dessie and Kombolcha City administrations, North-East Ethiopia: a population-based cross-sectional study. BMJ Open. 2020 Nov 1;10(11):e044202.
36. Iradukunda PG, Pierre G, Muhozi V, Denhere K, Dzinamarira T. Knowledge, Attitude, and Practice Towards COVID-19 Among People Living with HIV/AIDS in Kigali, Rwanda. J
43 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
Community Health [Internet]. 2020 Oct 26 [cited 2020 Dec 9]; Available from: https://doi.org/10.1007/s10900-020-00938-1
37. Chikoti L, Vundru WD, Fuje HN, Ilukor J, Kanyanda S, Kanyuka M, et al. Monitoring COVID-19 Impacts on Households in Malawi: Findings from the First Round of the High-Frequency Phone Survey [Internet]. Washington, DC: World Bank Group; 2020 Aug [cited 2020 Sep 30]. (Monitoring COVID-19 Impacts on Households in Malawi). Available from: http://documents.worldbank.org/curated/en/591551597706342578/Findings-from-the- First-Round-of-the-High-Frequency-Phone-Survey
38. Riley, Sully, Ahmed, Biddlecom. Estimates of the Potential Impact of the COVID-19 Pandemic on Sexual and Reproductive Health In Low- and Middle-Income Countries. International Perspectives on Sexual and Reproductive Health. 2020;46:73.
39. Harling G, Gómez-Olivé FX, Tlouyamma J, Mutevedzi T, Kabudula CW, Mahlako R, et al. Protective behaviours and secondary harms from non-pharmaceutical interventions during the COVID-19 epidemic in South Africa: a multisite prospective longitudinal study. medRxiv. 2020 Nov 15;2020.11.12.20230136.
40. Egger D, Miguel E, Warren SS, Shenoy A, Collins E, Karlan D, et al. Falling living standards during the COVID-19 crisis: Quantitative evidence from nine developing countries. Science Advances. 2021 Feb 1;7(6):eabe0997.
41. Anderson RM, May RM. Infectious Diseases of Humans: Dynamics and Control. OUP Oxford; 1992. 772 p.
42. Bai Y, Yao L, Wei T, Tian F, Jin D-Y, Chen L, et al. Presumed Asymptomatic Carrier Transmission of COVID-19. JAMA. 2020 Apr 14;323(14):1406–7.
43. Li G, Li W, He X, Cao Y. Asymptomatic and Presymptomatic Infectors: Hidden Sources of Coronavirus Disease 2019 (COVID-19). Clinical Infectious Diseases. 2020 Nov 5;71(8):2018– 2018.
44. Moghadas SM, Fitzpatrick MC, Sah P, Pandey A, Shoukat A, Singer BH, et al. The implications of silent transmission for the control of COVID-19 outbreaks. PNAS. 2020 Jul 28;117(30):17513–5.
45. Siemieniuk RA, Bartoszko JJ, Ge L, Zeraatkar D, Izcovich A, Kum E, et al. Drug treatments for covid-19: living systematic review and network meta-analysis. BMJ. 2020 Jul 30;370:m2980.
46. Amegah AK. Improving handwashing habits and household air quality in Africa after COVID- 19. The Lancet Global Health. 2020 Sep 1;8(9):e1110–1.
47. Ray I. Viewpoint – Handwashing and COVID-19: Simple, right there…? World Development. 2020 Nov 1;135:105086.
44 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
48. Lewnard JA, Lo NC. Scientific and ethical basis for social-distancing interventions against COVID-19. Lancet Infect Dis. 2020 Jun;20(6):631–3.
49. Greenhalgh T, Schmid MB, Czypionka T, Bassler D, Gruer L. Face masks for the public during the covid-19 crisis. BMJ. 2020 Apr 9;369:m1435.
50. Morawska L, Tang JW, Bahnfleth W, Bluyssen PM, Boerstra A, Buonanno G, et al. How can airborne transmission of COVID-19 indoors be minimised? Environment International. 2020 Sep 1;142:105832.
51. Hellewell J, Abbott S, Gimma A, Bosse NI, Jarvis CI, Russell TW, et al. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. The Lancet Global Health. 2020 Apr 1;8(4):e488–96.
52. Garba SM, Lubuma JM-S, Tsanou B. Modeling the transmission dynamics of the COVID-19 Pandemic in South Africa. Mathematical Biosciences. 2020 Oct 1;328:108441.
53. Zandvoort K van, Jarvis CI, Pearson CAB, Davies NG, Group CC-19 working, Russell TW, et al. Response strategies for COVID-19 epidemics in African settings: a mathematical modelling study. medRxiv. 2020 May 3;2020.04.27.20081711.
54. Quaife M, van Zandvoort K, Gimma A, Shah K, McCreesh N, Prem K, et al. The impact of COVID-19 control measures on social contacts and transmission in Kenyan informal settlements. BMC Medicine. 2020 Oct 5;18(1):316.
55. McCreesh N, Dlamini V, Edwards A, Olivier S, Dayi N, Dikgale K, et al. Impact of social distancing regulations and epidemic risk perception on social contact and SARS-CoV-2 transmission potential in rural South Africa: analysis of repeated cross-sectional surveys. medRxiv. 2020 Dec 3;2020.12.01.20241877.
56. Taboe HB, Salako KV, Tison JM, Ngonghala CN, Glèlè Kakaï R. Predicting COVID-19 spread in the face of control measures in West Africa. Mathematical Biosciences. 2020 Oct 1;328:108431.
57. Amodan BO, Bulage L, Katana E, Ario AR, Fodjo JNS, Colebunders R, et al. Level and Determinants of Adherence to COVID-19 Preventive Measures in the First Stage of the Outbreak in Uganda. Int J Environ Res Public Health. 2020 Nov 27;17(23).
58. Azene ZN, Merid MW, Muluneh AG, Geberu DM, Kassa GM, Yenit MK, et al. Adherence towards COVID-19 mitigation measures and its associated factors among Gondar City residents: A community-based cross-sectional study in Northwest Ethiopia. PLOS ONE. 2020 déc;15(12):e0244265.
59. Bonful HA, Addo-Lartey A, Aheto JMK, Ganle JK, Sarfo B, Aryeetey R. Limiting spread of COVID-19 in Ghana: Compliance audit of selected transportation stations in the Greater Accra region of Ghana. PLOS ONE. 2020 Sep 11;15(9):e0238971.
45 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
60. United Nations. World population prospects Volume 1, Volume 1,. 2019.
61. Crampin AC, Glynn JR, Ngwira BM, Mwaungulu FD, Pönnighaus JM, Warndorff DK, et al. Trends and measurement of HIV prevalence in northern Malawi. AIDS. 2003 Aug 15;17(12):1817–25.
62. Glynn JR, Pönnighaus J, Crampin AC, Sibande F, Sichali L, Nkhosa P, et al. The development of the HIV epidemic in Karonga District, Malawi. AIDS. 2001 Oct 19;15(15):2025–9.
63. Ministry of Health, Malawi. Malawi Population-Based HIV Impact Assessment (MPHIA) 2015-2016: Final Report. [Internet]. Lilongwe, Malawi: Ministry of Health; 2018 Oct [cited 2019 Dec 23]. Available from: https://phia.icap.columbia.edu/wp- content/uploads/2019/08/MPHIA-Final-Report_web.pdf
64. Chihana M, Floyd S, Molesworth A, Crampin AC, Kayuni N, Price A, et al. Adult mortality and probable cause of death in rural northern Malawi in the era of HIV treatment. Trop Med Int Health. 2012 Aug;17(8):e74-83.
65. Floyd S, Molesworth A, Dube A, Banda E, Jahn A, Mwafulirwa C, et al. Population-level reduction in adult mortality after extension of free anti-retroviral therapy provision into rural areas in northern Malawi. PLoS ONE. 2010 Oct 19;5(10):e13499.
66. Kanyuka M, Ndawala J, Mleme T, Chisesa L, Makwemba M, Amouzou A, et al. Malawi and Millennium Development Goal 4: a Countdown to 2015 country case study. The Lancet Global Health. 2016 Mar 1;4(3):e201–14.
67. Crampin AC, Kayuni N, Amberbir A, Musicha C, Koole O, Tafatatha T, et al. Hypertension and diabetes in Africa: design and implementation of a large population-based study of burden and risk factors in rural and urban Malawi. Emerg Themes Epidemiol. 2016;13:3.
68. Chilunga FP, Musicha C, Tafatatha T, Geis S, Nyirenda MJ, Crampin AC, et al. Investigating associations between rural-to-urban migration and cardiometabolic disease in Malawi: a population-level study. Int J Epidemiol. 2019 Oct 11;
69. Nakanga WP, Prynn JE, Banda L, Kalyesubula R, Tomlinson LA, Nyirenda M, et al. Prevalence of impaired renal function among rural and urban populations: findings of a cross-sectional study in Malawi. Wellcome Open Res. 2019;4:92.
70. Patel P, Adebisi YA, Steven M, Iii DEL-P. Addressing COVID-19 in Malawi. The Pan African Medical Journal [Internet]. 2020 Jun 9 [cited 2020 Dec 14];35(71). Available from: https://www.panafrican-med-journal.com/content/series/35/2/71/full/
71. Munharo S, Nayupe S, Mbulaje P, Patel P, Banda C, Gacutno KJA, et al. Challenges of COVID- 19 testing in low-middle income countries (LMICs): the case of Malawi. Journal of Laboratory and Precision Medicine [Internet]. 2020 Oct 30 [cited 2020 Dec 14];5(0). Available from: http://jlpm.amegroups.com/article/view/5808
46 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
72. Baraniuk C. Covid-19 contact tracing: a briefing. BMJ [Internet]. 2020 May 13 [cited 2020 Jul 6];369. Available from: https://www.bmj.com/content/369/bmj.m1859
73. Public Health Institute of Malawi. National COVID-19 Preparedness and Response Plan [Internet]. Lilongwe, Malawi: Public Health Institute of Malawi; 2020 [cited 2021 Feb 11]. Available from: https://malawipublichealth.org/index.php/resources/COVID- 19%20National%20Preparedness%20and%20Response%20Plan/National-COVID-19- Preparedness-and-Response-Plan_Version1_April%202020%201.pdf/detail
74. Speech by President Mutharika on 21-day Covid-19 lockdown | United Nations in Malawi [Internet]. [cited 2021 Feb 11]. Available from: https://malawi.un.org/en/46797-speech- president-mutharika-21-day-covid-19-lockdown
75. Chirwa GC, Dulani B, Sithole L, Chunga JJ, Alfonso W, Tengatenga J. Malawi at the Crossroads: Does the Fear of Contracting COVID-19 Affect the Propensity to Vote? Eur J Dev Res [Internet]. 2021 Jan 4 [cited 2021 Feb 11]; Available from: https://doi.org/10.1057/s41287-020-00353-1
76. Crampin AC, Dube A, Mboma S, Price A, Chihana M, Jahn A, et al. Profile: the Karonga Health and Demographic Surveillance System. Int J Epidemiol. 2012 Jun;41(3):676–85.
77. Jahn A, Branson K, Crampin A, Fine PE, Glynn JR, McGrath N, et al. Evaluation of a village- informant driven demographic surveillance system in Karonga, Northern Malawi. Demographic Research. 2007 Mar 23;16(8):219–48.
78. Banda J, Dube A, Brumfield S, Amoah A, Crampin A, Reniers G, et al. Knowledge and Behaviors Related to the COVID-19 pandemic in Malawi. medRxiv. 2020 Jun 29;2020.06.16.20133322.
79. Kao K, Lust E, Dulani B, Ferree KE, Harris AS, Metheney E. The ABCs of Covid-19 prevention in Malawi: Authority, benefits, and costs of compliance. World Development. 2021 Jan 1;137:105167.
80. Zhao D, Yao F, Wang L, Zheng L, Gao Y, Ye J, et al. A Comparative Study on the Clinical Features of Coronavirus 2019 (COVID-19) Pneumonia With Other Pneumonias. Clinical Infectious Diseases. 2020 Jul 28;71(15):756–61.
81. Wu Z, McGoogan JM. Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention. JAMA. 2020 Apr 7;323(13):1239–42.
82. Gautier J-F, Ravussin Y. A New Symptom of COVID-19: Loss of Taste and Smell. Obesity. 2020;28(5):848–848.
47 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
83. Mullol J, Alobid I, Mariño-Sánchez F, Izquierdo-Domínguez A, Marin C, Klimek L, et al. The Loss of Smell and Taste in the COVID-19 Outbreak: a Tale of Many Countries. Curr Allergy Asthma Rep. 2020 Aug 3;20(10):61.
84. Atchison, Bowman L, Vrinten C, Redd R, Pristera P, Eaton JW, et al. Perceptions and behavioural responses of the general public during the COVID-19 pandemic: A cross- sectional survey of UK Adults [Internet]. Public and Global Health; 2020 Apr [cited 2020 Jun 29]. Available from: http://medrxiv.org/lookup/doi/10.1101/2020.04.01.20050039
85. National Statistical Office. 2018 Malawi Population and Housing Census [Internet]. Zomba, Malawi; 2019 [cited 2020 Oct 5]. Available from: http://www.nsomalawi.mw/index.php%3Foption%3Dcom_content%26view%3Darticle%26i d%3D226:2018-malawi-population-and-housing- census%26catid%E2%80%89%3D%E2%80%898:reports%26Itemid%E2%80%89%3D%E2%80 %896
86. Roelen K, Ackley C, Boyce P, Farina N, Ripoll S. COVID-19 in LMICs: The Need to Place Stigma Front and Centre to Its Response. Eur J Dev Res. 2020 Oct 21;1–21.
87. Chibwana MG, Jere KC, Kamng’ona R, Mandolo J, Katunga-Phiri V, Tembo D, et al. High SARS-CoV-2 seroprevalence in health care workers but relatively low numbers of deaths in urban Malawi. Wellcome Open Res. 2020 Aug 25;5:199.
88. Majiya H, Aliyu-Paiko M, Balogu VT, Musa DA, Salihu IM, Kawu AA, et al. Seroprevalence of COVID-19 in Niger State. medRxiv. 2020 Aug 5;2020.08.04.20168112.
89. Uyoga S, Adetifa IMO, Karanja HK, Nyagwange J, Tuju J, Wanjiku P, et al. Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Kenyan blood donors. Science. 2021 Jan 1;371(6524):79– 82.
90. Gessner BD, Shindo N, Briand S. Seasonal influenza epidemiology in sub-Saharan Africa: a systematic review. Lancet Infect Dis. 2011 Mar;11(3):223–35.
91. Yusef D, Hayajneh W, Awad S, Momany S, Khassawneh B, Samrah S, et al. Large Outbreak of Coronavirus Disease among Wedding Attendees, Jordan. Emerg Infect Dis. 2020 Sep;26(9):2165–7.
92. Tegally H, Wilkinson E, Lessells RJ, Giandhari J, Pillay S, Msomi N, et al. Sixteen novel lineages of SARS-CoV-2 in South Africa. Nature Medicine. 2021 Feb 2;1–7.
93. Jakubowski A, Egger D, Nekesa C, Lowe L, Walker M, Miguel E. Self-Reported Mask Wearing Greatly Exceeds Directly Observed Use: Urgent Need for Policy Intervention in Kenya. medRxiv. 2021 Jan 29;2021.01.27.21250487.
48 medRxiv preprint doi: https://doi.org/10.1101/2021.02.21.21251597; this version posted February 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
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