Shi et al. Infect Dis Poverty (2021) 10:5 https://doi.org/10.1186/s40249-020-00788-y

RESEARCH ARTICLE Open Access Accessing the of COVID‑19 and malaria intervention in Africa Benyun Shi1† , Jinxin Zheng2,3,4,5,6,7†, Shang Xia2,3,4,5,6,7, Shan Lin8, Xinyi Wang2,3,4,5,6,7, Yang Liu9, Xiao‑Nong Zhou2,3,4,5,6,7 and Jiming Liu9*

Abstract Background: The of the coronavirus 2019 (COVID-19) has caused substantial disruptions to health services in the low and middle-income countries with a high burden of other , such as malaria in sub-Saharan Africa. The aim of this study is to assess the impact of COVID-19 pandemic on malaria potential in malaria- countries in Africa. Methods: We present a data-driven method to quantify the extent to which the COVID-19 pandemic, as well as vari‑ ous non-pharmaceutical interventions (NPIs), could lead to the change of malaria transmission potential in 2020. First, we adopt a particle Markov Chain Monte Carlo method to estimate epidemiological parameters in each country by ftting the time series of the cumulative number of reported COVID-19 cases. Then, we simulate the dynam‑ ics of COVID-19 under two groups of NPIs: (1) contact restriction and social distancing, and (2) early identifcation and isolation of cases. Based on the simulated epidemic curves, we quantify the impact of COVID-19 epidemic and NPIs on the distribution of insecticide-treated nets (ITNs). Finally, by treating the total number of ITNs available in each country in 2020, we evaluate the negative efects of COVID-19 pandemic on malaria transmission potential based on the notion of vectorial capacity. Results: We conduct case studies in four malaria-endemic countries, Ethiopia, Nigeria, Tanzania, and Zambia, in Africa. The epidemiological parameters (i.e., the basic reproduction number R0 and the duration of DI ) of COVID-19 in each country are estimated as follows: Ethiopia ( R0 = 1.57 , DI = 5.32 ), Nigeria ( R0 = 2.18 , DI = 6.58 ), Tan‑ zania ( R0 = 2.47 , DI = 6.01 ), and Zambia ( R0 = 2.12 , DI = 6.96 ). Based on the estimated epidemiological parameters, the epidemic curves simulated under various NPIs indicated that the earlier the interventions are implemented, the better the epidemic is controlled. Moreover, the efect of combined NPIs is better than contact restriction and social distancing only. By treating the total number of ITNs available in each country in 2020 as a baseline, our results show that even with stringent NPIs, malaria transmission potential will remain higher than expected in the second half of 2020. Conclusions: By quantifying the impact of various NPI response to the COVID-19 pandemic on malaria transmis‑ sion potential, this study provides a way to jointly address the syndemic between COVID-19 and malaria in malaria- endemic countries in Africa. The results suggest that the early intervention of COVID-19 can efectively reduce the scale of the epidemic and mitigate its impact on malaria transmission potential.

*Correspondence: [email protected] †Benyun Shi and Jinxin Zheng equally contribute to the work 9 Department of Computer Science, Hong Kong Baptist University, Hong Kong, China Full list of author information is available at the end of the article

© The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativeco​ mmons​ .org/licen​ ses/by/4.0/​ . The Creative Commons Public Domain Dedication waiver (http://creativeco​ ​ mmons.org/publi​ cdoma​ in/zero/1.0/​ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Shi et al. Infect Dis Poverty (2021) 10:5 Page 2 of 12

Keywords: COVID-19 pandemic, Non-pharmaceutical interventions, Particle Markov chain Monte Carlo, Insecticide- treated nets, Vectorial capacity, Malaria transmission potential

Background around exposure to COVID-19.1 It is estimated that In 2020, the coronavirus disease 2019 (COVID-19) under the worst situation, where all ITN campaigns were caused by severe acute respiratory syndrome coronavirus suspended, malaria deaths in sub-Saharan Africa in 2020 2 (SARS-CoV-2) spread across the world and resulted in would reach an estimated 769 000 [15]. a pandemic [1–3]. Even though great eforts have been In this study, we aim to assess the syndemic of COVID- made, the pandemic has continued and worsened in 19 and malaria intervention in four malaria-endemic some countries. As of June 29, 2020, it has caused more countries in Africa: Ethiopia, Nigeria, Tanzania, and than 10 million confrmed cases and 499 913 deaths in Zambia. First, we adopt a particle Markov Chain Monte as many as 213 countries and territories [4]. To contain Carlo (PMCMC) method to estimate the epidemiological the global spread of COVID-19, a set of non-pharma- parameters in each country [16]. Based on the estimated ceutical interventions (NPIs) have been suggested and epidemiological parameters, we can then simulate the implemented, such as isolation of ill persons, quaran- epidemic curves of the COVID-19 in each country under tine of exposed persons, contact tracing, travel restric- various non-pharmaceutical interventions (NPIs) in tions, school and workplace closures, and cancellation terms of when and how diferent NPIs are implemented. of mass gatherings [5, 6]. It is estimated that more than Existing studies have shown that NPIs play essential roles 138 countries have closed schools nationwide, and sev- in fghting the COVID-19 epidemic [5]. For example, eral other countries have implemented regional or local Lai et al. have studied the efects of three major groups closures [7]. An integrative literature review has shown of NPIs on containing the spread of COVID-19 across that the COVID-19 pandemic may have a great socio- China [6], including (1) contact restrictions and social economic impact on global poverty [8]. Te entire world distancing, (2) early identifcation and isolation of poten- is facing a human, economic and social crisis [9–11]. tial cases, and (3) inter-city travel restriction. Because in According to the World Health Organization, there is this study we focus mainly on the spread of COVID-19 an urgent need to aggressively tackle the COVID-19 in Africa at the country level, we only conduct scenario pandemic while ensuring that other diseases, such as analysis on the frst two groups of NPIs in the four coun- malaria, are not neglected. tries. Moreover, to quantify the impact of COVID-19, we Malaria is a mosquito-borne infectious disease, which assume that the distribution of ITNs is afected by the has long been a nightmare for countries in sub-Saha- severity of the COVID-19 epidemic. Te severer the epi- ran Africa. Based on the WHO’s world malaria report demic, the greater the impact on the distribution of ITNs. 2019, sub-Saharan Africa accounted for about 93% of Tere is no doubt that the cessation or disruption of all malaria cases and 94% of deaths in 2018, from an ITN distribution will result in an increase in the human estimated 228 million cases and 405 000 deaths world- biting rate, and further the transmission potential of wide [12]. As of March 12, 2020, several malaria-endemic malaria. Specifcally, in this study, we adopt the notion of regions in Africa have reported a few imported COVID- vectorial capacity (VCAP) to characterize the transmis- 19 cases, which has led to serious public health emer- sion potential of a mosquito population in the absence of gencies. As the COVID-19 pandemic spreads in Africa, Plasmodium, which is defned as the number of poten- it becomes a challenging task to maintain core malaria tially infective contacts a person makes, through the mos- control services while protecting health workers against quito population, per day [17, 18]. Since 2012, Ceccato COVID-19 transmission [13]. As a consequence, malaria et al. have proposed a VCAP product to monitor malaria control services have been disrupted in many malaria- transmission potential in epidemic regions in Africa, endemic regions in sub-Saharan Africa, among which where the value of VCAP is calculated by the dynami- insecticide-treated net (ITN) campaigns have been cally changing meteorological factors, i.e., temperature considered as the most important measure for malaria and rainfall [19]. Based on the historical VCAP data, we intervention and control across Africa in the last two can analyze the seasonal patterns of malaria transmission decades [14]. However, as of March 2020, there have potential in each country. By treating the number of ITNs been reports of the suspension of insecticide-treated net campaigns in several African countries due to concerns

1 A WHO Statement: https://www.who.int/emerg​ encie​ s/disea​ ses/novel​ -coron​ ​ aviru​s-2019/quest​ion-and-answe​rs-hub/q-a-detai​l/malar​ia-and-the-covid​-19- pandemic​ . Shi et al. Infect Dis Poverty (2021) 10:5 Page 3 of 12

Fig. 1 An illustration of the cumulative number of reported COVID-19 cases and the of malaria infection in Africa. The left shows the total number of reported COVID-19 cases till June 30, 2020. The right shows the incidence of malaria infection (per 1000 population at risk) in 2018. The fgure was generated using the Free Software R with version 3.6.3

distributed in 2019 as a benchmark, we can then access index​.html). Finally, to quantify the impact of COVID- and predict the impact of cessation or disruption of ITN 19 pandemic on malaria transmission potential, we distribution on malaria transmission potential under have also collected the population size, the ITN cov- diferent NPI scenarios in each country. In this study, erage, and the total number of ITNs available for each we aim to quantify the extent to which the COVID-19 country. Based on the Malaria Indicator Survey (MIS) pandemic in Ethiopia, Nigeria, Tanzania, and Zambia in in each country, the ITN coverage rate before 2020 is Africa with high malaria burden could lead to the change estimated by the percentage of de facto household of malaria transmission potential in 2020. population who slept under an ITN the night before the survey. While the total number of ITNs available Methods in 2020 can be obtained from the Malaria Operational Country selection and data sources Figure 1 illustrates the cumulative number of reported COVID-19 cases and the incidence of malaria infec- Table 1 The data used for assessing the impact of COVID- tion in Africa. Te four countries are located in east- 19 pandemic in Ethiopia, Nigeria, Tanzania, and Zambia ern Africa, western Africa, and central and southern Country Populationa COVID- Malariac ITN ITNs Africa, where the infection risk of both COVID-19 and 19b coverage­ d available malaria is relatively high. First, to estimate the epidemi- in 2020­ e ological parameters of COVID-19 in each country, we use the time series of cumulative COVID-19 cases in Ethiopia 109 224 414 5846 31.814 39.70% 4 723 087 Nigeria 195 874 683 25 133 291.194 54.70% 11 300 each country till June 2, 2020. Te reported COVID-19 000 cases in each country are collected from the website of Tanzania 56 313 438 509 124.265 52.20% 7 559 527 the World Health Organization (Source: https​://covid​ Zambia 17 351 708 1568 156.701 63.90% 1 988 000 19.who.int/). Ten, we characterize the annual pattern a Population size: https​://www.world​omete​rs.info/popul​ation​/count​ries-in-afric​ of malaria transmission potential in each country based a-by-popul​ation​/ on the historical values of VCAP from January 1, 2004, b The reported COVID-19 cases on June 30, 2020: https​://covid​19.who.int/ to December 31, 2019. Te values of VCAP are down- c Incidence of malaria (per 1000 population at risk): https​://data.world​bank.org/ loaded from the International Research Institute for indic​ator/SH.MLR.INCD.P3 Climate and Society (Source: http://iridl​.ldeo.colum​bia. d Malaria Indicator Survey: https​://www.malar​iasur​veys.org edu/mapro​om/Healt​h/Regio​nal/Afric​a/Malar​ia/VCAP/ e Malaria Operational Plan (FY 2019): https​://www.pmi.gov/resou​rce-libra​ry/ mops/fy-2019 Shi et al. Infect Dis Poverty (2021) 10:5 Page 4 of 12

Plan (FY 2019) of each country. All data and their country. According to the Bayesian inference method, sources are summarized in Table 1. uninformative uniform priors are assigned to model parameters to reduce their infuence on the posteriors, Estimating epidemiological parameters of COVID‑19 that is, R0 ∼ U(0, 8) , DI ∼ U(1, 20) , and d ∼ U(0, 30) . pandemic Since the interval of each prior covers almost all possi- To accurately predict the trend of COVID-19 pandemic, ble values of the corresponding parameter, such settings we frst estimate the epidemiological parameters of have little efect on the inference results as long as the COVID-19 by ftting the time series of reported COVID- number of iterations is enough. With careful pre-test- 19 cases in each country. Here, the time series is about ing, we set the proposal distribution of each parameter ∗ ∗ the reported date of the COVID-19 as of June to be normal distribution: q(R0|R0) = norm(R0|R0, 0.5) , ∗ ∗ , 0.5 2, 2020. Evidence has shown that infec- q(DI |DI ) = norm(DI |DI ) , and ∗ ∗ tions play essential roles in the spread of COVID-19. q(d |d) = norm(d |d, 0.5) . After initializing the values However, due to limited public health resources in the of model parameters as R0 = 2.5 , DI = 5 , and d = 14 , four countries in Africa, it is difcult to identify asymp- we run the PMCMC algorithm with 200 particles for tomatic infections in the early stages of the epidemic. In 100,000 iterations. Finally, the posterior of each param- this case, following existing studies [26, 27], we resort to eter is built upon the last 80% iterations with a dis- the classical susceptible–exposed–infectious–removed carded burn-in of 20,000 iterations. (SEIR) model to simulate the transmission dynamics of COVID-19 in a population of size N: Simulating epidemic dynamics of COVID under diferent dS(t) =−R0 · S(t)I(t) dt DI N NPIs  dE(t) = R0 · S(t)I(t) − E(t)  dt DI N DE Based on the estimated model parameters, we simulate  dI(t) = E(t) − I(t) (1)  dt DE DI the dynamics of COVID-19 under two groups of NPIs: dR(t) = I(t) (1) contact restriction and social distancing (e.g., con-  dt DI  tact restrictions and personal preventive actions), and where S(t), E(t), I(t), and R(t) represent the number of (2) early identifcation and isolation of cases. Generally susceptible, exposed, infectious, and removed individu- speaking, contact restrictions can reduce the infectious β als at time t. In this study, we assume that the reported rate of COVID-19; while early identifcation and iso- COVID-19 cases are removed or quarantined from the lation of potential cases can reduce the average dura- D population and can no longer infect others. Along this tion of infection I . On the one hand, we assess the line, the time series of reported cases we observed is efects of social distancing interventions by reducing β actually the states of R(t) over time. the estimated infectious rate to 25% and 10%, while Tere are three epidemiological parameters in the SEIR keeping the duration of infection unchanged. Tis is R0 model: R0 is the basic reproduction number; DE is the equivalent to reducing to 25% and 10% of its origi- average latent period; and DI is the average contagious nal value. On the other hand, we also evaluate the period (i.e., the average duration that an infectious indi- impact of combined intervention strategies, where both vidual is confrmed to be infected). Following the study social distancing and early identifcation and isolation in [26], we assume that the latent period is the same as are implemented. Specifcally, the epidemic dynamics R0 the incubation period. Further, based on the estimation of COVID-19 are simulated when reducing to 25% D in [1], the mean incubation period of COVID-19 was 5.2 and I to 2 (or 4) at the same time. In addition to what days. In this case, we set DE = 5.2 . Te infectious rate, types of NPIs are implemented, it is also important β = R0/DI , controls the rate of spread that represents the when to implement the interventions. Accordingly, we probability of transmitting disease between a susceptible further simulate the epidemic dynamics of COVID-19 and an infectious individual. In this study, we adopt the under various settings of NPIs that are implemented on particle Markov Chain Monte Carlo method to estimate May 18 and June 17, 2020, respectively. epidemiological parameters R0 and DI in each country by ftting the time series of the cumulative number of Analyzing malaria transmission potential from vectorial reported COVID-19 cases [16, 20]. capacity We implement the PMCMC method and simulate the COVID-19 epidemic using python programming lan- We adopt the notion of vectorial capacity to evaluate guage version 3.8.5 (source: https​://www.pytho​n.org/). malaria transmission potential in malaria-endemic coun- We assume that the frst exposed case [i.e., E(0)] appears tries in Africa. Based on the Macdonald model [21], the d days before the date of the frst reported case in each VCAP can be formulated as: Shi et al. Infect Dis Poverty (2021) 10:5 Page 5 of 12

2 2 ma e−gn −ma pn Vi(t) � (t) = � , V = = , (2) i i (3) g ln p t Vi(t) where m is the average mosquito density per person; a is where i represents the total number of available ITNs in the expected number of bites on humans per mosquito, country i throughout 2020, and Vi(t) represents the mean per day (i.e., human feeding rate); g is the per-capita daily value of 16-year VCAP in time interval t of each year. death rate of a mosquito (i.e., the force of mortality); n is As the number of newly reported COVID-19 cases −g � (t) = R (t) − R (t − 1) the sporogonic cycle length of the Plasmodium; p = e Ri i i increases, it is assumed that represents the probability of a mosquito survives through the distribution of ITNs will be disrupted accordingly. one whole day. Conceptually, the VCAP incorporates all Moreover, when the COVID-19 becomes serious (e.g., information about mosquito population (e.g., human bit- the number reaches a threshold value τ ), the distribution ing rate, life expectancy), which is defned as the number of ITNs will be suspended. Mathematically, we assume of potentially infective contacts a person makes, through that the number of distributed ITNs in time interval t, the mosquito population, per day. Many studies have Di(t) , is inversely proportional to the number of reported shown that the value of VCAP can be calculated based COVID-19 cases. Tus, we have: on meteorological factors, such as temperature and pre- 1 , if , ( − �Ri (t)/τ)�i(t) �Ri (t)<τ cipitation [22, 23]. For example, Ceccato et al. have cal- Di(t) = 0, otherwise. (4) culated the average vectorial capacity per 8 days for areas where malaria is considered to be an epidemic in 1 Let Di( : t) = t Di(t) represent the cumulative num- Africa [19]. If there is no abnormal climate change, the ber of distributed ITNs from the frst time interval to the annual pattern of malaria transmission potential in each tth interval in 2020. Ten, the newly increased ITN cov- country should be relatively stable across diferent years. erage rate till time interval t becomes Di(1 : t)/Ni , where On this basis, we download and extract the 8-day aver- Ni is the population size of country i. For case studies in age vectorial capacity for each county from January 1, each of the four African countries, the threshold value τ 2004, to December 31, 2019. We then use the means of is set to be the number of reported cases when various the 16-year VCAP as a baseline of the annual pattern of NPIs are implemented. malaria transmission potential. Te disruption or cessation of distribution of ITNs may reduce the expected ITN coverage in a country, which may lead to the increase in human feeding rate a, as well Accessing the impact of COVID‑19 response on malaria as the transmission potential of malaria. Denote Ci as the transmission potential ITN coverage rate in country i before 2020. In this study, In this study, we focus mainly on assessing the impact we treat Ci as a reference value, which corresponds to of COVID-19 response on the disruption of ITN dis- human feeding rate of the baseline value of VCAP. Ten, tribution, and further on the transmission potential if all available ITNs are distributed as expected, the rela- of malaria. Launched in 2005, the President’s Malaria tive change of human feeding rate can be estimated as Initiative (PMI) strives to reduce the burden of malaria follows: across 15 high-burden countries in sub-Saharan Africa 1 − α(C + � (1 : t)/N ) through a rapid scale-up of four proven and highly exp = i i i , ri (t) (5) efective malaria prevention and treatment measures, 1 − αCi including insecticide-treated mosquito nets. In most α countries, the PMI has supported ITN distribution where indicates the efciency of ITNs against mosquito through universal mass campaigns and continuous dis- bites. According to the defnition of vectorial capacity, tribution channels. Based on the Malaria Operational the expected transmission potential of malaria at time Plan (FY 2019) in each country, we can obtain the total interval t can be calculated as: ITNs available from diferent partner contributions in exp exp 2 . Vi (t) = (ri (t)) · Vi(t) (6) 2020 (see Table 1). In this study, we assume that the available ITNs are distributed throughout the year in Similarly, if the distribution of ITNs is disrupted, the rela- a way that the number of distributed ITNs is propor- tive change of human feeding rate is: tional to the annual pattern of malaria transmission 1 − α(C + D (1 : t)/N ) potential in each time interval (eight days in this study). rdis(t) = i i i , i 1 − (7) In doing so, the newly increased number of ITNs in a αCi specifc time interval t in 2020 can be estimated as: and the transmission potential becomes: Shi et al. Infect Dis Poverty (2021) 10:5 Page 6 of 12

Ethiopia: R 0 = 1.57 (95% CI,1.35–1.80 ) Ethiopia: D I = 5.32 (95% CI,2.68–7.96 ) Ethiopia: fitting curve 2500 Fitted 1500 1200 2000 Observed

1500 1000 800

1000 Densit y Densit y 500 400

500 COVID-19 case s

0 0 0 1.52.0 2.53.0 51015 2020-04-01 2020-05-01 2020-06-01 R 0 D I Time

Nigeria: R 0 = 2.18 (95% CI,1.75–2.61 ) Nigeria: D I = 6.58 (95% CI,3.69–9.47 ) Nigeria: fitting curve 1500 1500 20000

1000 15000 1000

10000 Densit y Densit y 500 500

COVID-19 case s 5000

0 0 0 234 5101520 2020-04-01 2020-05-012020-06-01 R 0 D I Time

Tanzania: R 0 = 2.47 (95% CI,1.78–3.16 ) Tanzania: D I = 6.01 (95% CI,2.93–9.09 ) Tanzania: fitting curve 1250 600 1000 1000 s 750 400 750

500 500 Densit y Densit y 200

250 COVID-19 case 250

0 0 0 246 5101520 2020-04-01 2020-4-15 2020-05-01 R 0 D I Time

Zambia: R 0 = 2.12 (95% CI,1.63–2.61 ) Zambia: D I = 6.96 (95% CI,3.51–10.40 ) Zambia: fitting curve 1250 2000

1000 750 1500

750 500 1000 500 Densit y Densit y

250 500 250 COVID-19 case s

0 0 0 234 5101520 2020-04-01 2020-05-01 2020-06-01 R 0 D I Time Fig. 2 The inferred epidemiological parameters and the ftting results in Ethiopia, Nigeria, Tanzania, and Zambia. The parameters R0 and DI are estimated using the particle Markov Chain Monte Carlo method by ftting the time series of cumulative number of reported COVID-19 cases in each country till June 2, 2020. The frst two columns shows the density and mean values of R0 and DI in each country. The third column shows the ftting curves (red lines) and the cumulative cases (blue lines) of COVID-19 in each country

dis dis 2 . Results Vi (t) = (ri (t)) · Vi(t) (8) Reconstruction of COVID‑19 dynamics in each country In this study, we set α = 1 . Note that if the ITN distribu- In this study, we adopt the PMCMC method to estimate exp dis tion is disrupted, we have Vi (t)

demonstrates the inferred model parameters and the R0 to 25% and 10% of its estimated value, while keep- ftting results in Ethiopia, Nigeria, Tanzania, and Zam- ing the duration of infection DI unchanged. In doing so, bia, respectively. Te sampled posterior distributions the infectious rate β is reduced to 25% and 10%, respec- of R0 and DI are shown in the frst two columns. It can tively. It is noteworthy that both NPIs are very stringent. be observed that in diferent countries, epidemiological For example, even if R0 is reduced to 25%, it becomes characteristics varies greatly. In Ethiopia, the estimated R0 = 0.6175 in Tanzania and R0 = 0.3925 in Ethiopia. basic reproduction number R0 is 1.57 [95% confdence Intuitively, the more stringent the intervention meas- interval (CI) 1.35–1.8] and the average duration of infec- ures, the easier it is to control the epidemic, and its infu- tion DI is 5.32 days (95% CI 2.68–7.96); In Nigeria, the ence on malaria intervention measures will gradually estimated R0 is 2.18 (95% CI 1.75–2.61) and the esti- be weakened. As shown in the left column of Fig. 3, the mated DI is 6.58 days (95% CI 3.69–9.47); In Tanzania, solid lines always reach zero cases earlier than the dashed the estimated R0 is 2.47 (95% CI 1.78–3.16) and DI is lines. Moreover, the earlier the intervention is imple- 6.01 days (95% CI 2.93–9.09); In Zambia, the estimated mented, the smaller the peak number of infections, and R0 is 2.12 (95% CI 1.63–2.61) and DI is 6.96 days (95% the sooner the peak comes. Figure 3 indicates that if the CI 3.51–10.40). Te bell-shaped posterior distribution NPIs are implemented one month late, the number of of parameter samples indicates a good estimation of infections will increase exponentially. model parameters in the four countries. In reality, at the Te second column of Fig. 3 shows the cumulative beginning of the epidemic, the COVID-19 cases are not number of reported COVID-19 cases under combined reported on a daily basis. Terefore, there are many zeros NPIs, where R0 is reduced to 25%, and DI is reduced in the time series of newly reported cases. It is notewor- to two or four days. Comparing with the NPI of reduc- thy that zero cases in a day do not mean that there are ing R0 to 25% only, the combined NPIs cannot decrease no new infections. More likely, new infections in that day the total number of infections. Nevertheless, when the will be reported a few days later. In this case, it becomes duration of infection DI is smaller (e.g., DI = 2 days), difcult to accurately ft the true number of reported the epidemic will be controlled faster than the NPI cases. Te third column in Fig. 2 shows the ftting curves with a larger DI . Meanwhile, the peak of infection (i.e., with 95% confdence interval of the cumulative number of the infection point) will also come earlier. It can be COVID-19 cases in each country. We measure the good- observed from Fig. 3 that the combined NPI with DI = 2 ness of ft using the root mean squared error (RMSE), the (i.e., the solid lines) can control the epidemic faster than value of which is 111.22 for Ethiopia, 1263.36 for Nige- that with DI = 4 (i.e., the dashed lines) when they were ria, 66.69 for Tanzania, and 124.37 for Zambia. Because implemented on the same day. In this case, if the ITNs the COVID-19 infections in these countries are not are distributed throughout the year, the time duration it reported in time, the values of RMSE are relatively high. will be afected by the epidemic will be shorter. Hence, However, it can be observed that the ftting belts gener- the combined NPIs may reduce the impact of COVID- ated by the PMCMC algorithm can well cover the time 19 epidemic on the distribution of ITNs, and further the series of cumulative cases in most situations. Moreover, transmission potential of malaria. it must be pointed out that no new COVID-19 cases have been reported in Tanzania since May 8, 2020. Although Impact of COVID‑19 pandemic on malaria transmission the ftting curve can well cover the cumulative number potential of reported cases, the basic reproduction number R0 of In this study, we adopt the notion of vectorial capacity to Tanzania is likely underestimated. While in Ethiopia, the evaluate the transmission potential of malaria. To quan- number of reported cases dramatically increases after tify the impact of COVID-19, we frst analyze the annual May 29, 2020. As shown in Fig. 2, the ftting curve is pattern of the transmission potential of malaria. Te slightly lower than the cumulative number of reported frst column in Fig. 4 shows the mean values of VCAP COVID-19 cases. As a consequence, R0 of Ethiopia may and corresponding confdence intervals in each country. also be underestimated. It can be observed that the annual patterns of VCAP in diferent countries vary greatly due to diferent meteoro- Simulation of COVID‑19 epidemic under diferent logical conditions throughout a year. Intuitively, if the non‑pharmaceutical interventions COVID-19 epidemic occurs during the malaria trans- We simulate the epidemic curves of COVID-19 in Ethio- mission season, it will seriously disrupt health services pia, Nigeria, Tanzania, and Zambia under various types for routine malaria intervention and control. Terefore, of NPIs implemented on May 18 and June 17, 2020. First, we further quantify the expected reduction of malaria the NPI of contact restriction and social distancing is transmission potentialexp without COVID-19 pandemic evaluated by reducing the basic reproduction number [i.e., Vi(t) − Vi (t) ] based on ITNs available in each Shi et al. Infect Dis Poverty (2021) 10:5 Page 8 of 12

Ethiopia: contact restriction and social distancing Ethiopia: early identification and isolation 60 Intervention date Intervention date 2000 2020-05-18 Jun 17 2020-05-18 2020-06-17 2020-06-17

40 R 0 reduce to 10% 1500 R 0 reduce to 25% R 0 reduce to 25% D I =2 days

D I =4 days

1000 Jun 17

20 COVID-19 case s COVID-19 case s May 18 500 May 18

0 0 2020-04-01 2020-07-01 2020-10-01 2020-04-01 2020-06-01 2020-08-01 2020-10-01 2020-12-01 Time Time Nigeria: contact restriction and social distancing Nigeria: early identification and isolation

4000 Intervention date Intervention date 150000 2020-05-18 Jun 17 2020-05-18 2020-06-17 2020-06-17

s 3000 s R 0 reduce to 10% R 0 reduce to 25% R reduce to 25% 0 100000 D I =2 days D I =4 days 2000

50000 Jun 17 COVID-19 case 1000 COVID-19 case

May 18 May 18 0 0 2020-04-01 2020-06-01 2020-08-01 2020-10-01 2020-12-01 2020-04-01 2020-06-01 2020-08-01 2020-10-01 2020-12-01 Time Time Tanzania: contact restriction and social distancing Tanzania: early identification and isolation

Intervention date 120000 Intervention date 2020-05-18 3000 2020-05-18 2020-06-17 2020-06-17 Jun 17 R 0 reduce to 10% R 0 reduce to 25% R 0 reduce to 25% 80000 D I =2 days

2000 D I =4 days

40000

COVID-19 cases 1000 COVID-19 cases Jun 17

May 18 May 18 0 0 2020-04-01 2020-06-01 2020-08-01 2020-10-01 2020-12-01 2020-04-01 2020-06-01 2020-08-01 2020-10-01 2020-12-01 Time Time Zambia: contact restriction and social distancing Zambia: early identification and isolation

Intervention date 300 Jun 17 Intervention date 2020-05-18 2020-05-18 2020-06-17 2020-06-17 10000 R 0 reduce to 10% R 0 reduce to 25% R reduce to 25% 200 0 D I =2 days D I =4 days

5000 100 Jun 17 COVID-19 cases COVID-19 cases

May 18 May 18 0 0 2020-04-01 2020-06-01 2020-08-01 2020-10-01 2020-12-01 2020-04-01 2020-06-01 2020-08-01 2020-10-01 2020-12-01 Time Time Fig. 3 The simulation of COVID-19 epidemic in Ethiopia, Nigeria, Tanzania, and Zambia under diferent NPIs implemented on May 18 and June 17, 2020. The left column shows the number of newly-reported COVID-19 cases over time under the NPI of contact restriction and social distancing, where R0 is reduced to 25% and 10% of its original value. The right column shows the cumulative number of reported COVID-19 cases under combined NPIs, where R0 is reduced to 25% and DI is reduced to two or four days Shi et al. Infect Dis Poverty (2021) 10:5 Page 9 of 12

Ethiopia: annual pattern of VCAP Ethiopia: transmission potential Ethiopia: negative effects of NPIs 0.15 1.00 Implemented on June 17,2020 Implemented on June 17,2020 No Intervention No Intervention R 0 reduce to 25% R reduce to 25% 2 0.75 0 R 0 reduce to 25% D I =2 R reduce to 25% D =2 0.10 Expected reduction of VCAP 0 I

0.50

VCAP 1 ∆ VCAP 0.05 0.25 Negative effect s

0 0.00 0.00 2020-03-01 2020-06-01 2020-09-012020-12-01 2020-03-01 2020-06-01 2020-09-01 2020-12-01 2020-03-01 2020-06-01 2020-09-01 2020-12-01 Time Time Time Nigeria: annual pattern of VCAP Nigeria: transmission potential Nigeria: negative effects of NPIs 1.2 15 1.00 Implemented on June 17,2020 Implemented on June 17,2020 No Intervention No Intervention R 0 reduce to 25% 0.9 R 0 reduce to 25% R 0 reduce to 25% D I =2 0.75 R 0 reduce to 25% D I =2 10 Expected reduction of VCAP

0.6 0.50 VCAP 5 ∆ VCAP 0.3 0.25 Negative effect s

0 0.0 0.00 2020-03-01 2020-06-01 2020-09-012020-12-01 2020-03-012020-06-01 2020-09-01 2020-12-01 2020-03-012020-06-01 2020-09-01 2020-12-01 Time Time Time Tanzania: annual pattern of VCAP Tanzania: transmission potential Tanzania: negative effects of NPIs Implemented on June 17,2020 1.00 8 Implemented on June 17,2020 No Intervention No Intervention 1.5 R 0 reduce to 25% R reduce to 25% 0 R 0 reduce to 25% D I =2 0.75 R reduce to 25% D =2 Expected VCAP in 2020 0 I 6 1.0 0.50 VCAP

4 ∆ VCAP 0.5 0.25 Negative effect s

2 0.0 0.00 2020-03-01 2020-06-01 2020-09-012020-12-01 2020-03-012020-06-01 2020-09-01 2020-12-01 2020-03-012020-06-01 2020-09-01 2020-12-01 Time Time Time Zambia: annual pattern of VCAP Zambia: transmission potential Zambia: negative effects of NPIs 1.00 8 Implemented on June 17,2020 Implemented on June 17,2020 No Intervention 1.5 No Intervention R 0 reduce to 25% R reduce to 25% 0 6 R 0 reduce to 25% DI =2 0.75 R reduce to 25% D =2 Expected VCAP in 2020 0 I 1.0 4 0.50 VCAP ∆ VCAP 2 0.5 0.25 Negative effect s

0 0.0 0.00 2020-03-01 2020-06-01 2020-09-012020-12-01 2020-03-012020-06-01 2020-09-01 2020-12-01 2020-03-012020-06-01 2020-09-01 2020-12-01 Time Time Time Fig. 4 The impact of various NPIs for COVID-19 pandemic on the transmission potential of malaria in 2020. The frst column shows the annual pattern of VCAP and corresponding confdence interval in each country, which is treated as malaria transmission potential through a year. The second column shows the reduction of transmission potential through ITN distribution under various NPIs implemented on June 17, 2020. The third column shows the negative efects of COVID-19 pandemic under various NPIs that cannot achieve the desired reduction in transmission potential of malaria. The gray bars are the real number of reported COVID-19 cases

country in 2020. Specifcally, based on the simulated Nigeria will be distributed during the malaria transmis- COVID-19 epidemic under various NPIs, we estimate sion reason from June to October. While in Tanzania and the reduction of malaria transmission potential through- Zambia, the reduction of malaria transmission potential out 2020. Te results are shown in the second column in will gradually increase in the second half of the year. Tis Fig. 4. Te purple lines show the expected reduction of is because some ITNs will be distributed in the frst half malaria transmission potential when the COVID-19 pan- of the year, and the cumulative number of ITNs will play demic did not occur. It can be observed that in Ethiopia an important role in reducing the malaria transmission and Nigeria, the trend of reduction is generally consist- potential in the second half of the year. ent with the annual pattern of VCAP. Te reason is that In addition, the second column in Fig. 4 also shows the based on our assumption, most ITNs in Ethiopia and diference of VCAP compared to the expected VCAP Shi et al. Infect Dis Poverty (2021) 10:5 Page 10 of 12

dis exp (i.e., �Vi(t) = Vi (t) − Vi (t) ) caused by various NPIs countries [24, 25]. Even in the same country, the epidemi- implemented on June 17, 2020. It can be observed that in ological parameters measured with diferent data at dif- the frst quarter of 2020, there is no diference between ferent stages may also be diferent. For example, Li et al. the red, yellowgreen, and turquoise lines in each coun- have analyzed the frst 425 diagnosed COVID-19 cases in R0 try because no COVID-19 cases are reported. As the Wuhan and found that is about 2.2 [1]. Ten, Wu et al. COVID-19 epidemic becomes more and more serious, have combined international aviation data and domestic R0 the diference �Vi(t) begins to grow in Tanzania and Tencent mobile data to estimate that the of COVID- Zambia due to its negative efect on the distribution of 19 is around 2.68 [26]. Tian et al. have analyzed through ITNs. On the contrary, in Ethiopia and Nigeria, the difer- diferent stages of the COVID-19 epidemic in China: ence becomes large during the malaria season (i.e., from before the emergency response was initiated on January R0 June to October) due to the simultaneous transmission of 23, was 3.15; since January 23, due to the expansion of R0 COVID-19 and malaria. When the COVID-19 epidemic the scope of prevention and control measures, the in is controlled and ITNs are distributed as scheduled after diferent provinces has declined; when the intervention R0 October, the malaria transmission season is over. As a coverage reaches 95%, the average drops to 0.04 [27]. consequence, the distributed ITNs cannot contribute to In this study, based on the time series of COVID-19 malaria intervention and control this year anymore. cases as of June 2, 2020, we have employed the classical Te third column in Fig. 4 shows the negative efects SEIR model to simulate the transmission dynamics of of COVID-19 pandemic under various NPIs that can- COVID-19, and adopted a PMCMC method to estimate not achieve the desired reduction in the transmission the epidemiological parameters by ftting the cumulative potential of malaria. Here, the negative efect is defned number of reported COVID-19 cases in each country. dis exp exp as (Vi (t) − Vi (t))/(Vi(t) − Vi (t)) , which indicates Comparing with the early stage of COVID-19 spread in R0 D the proportion of VCAP reduction that cannot com- China, the estimated and I are both in a reasonable pleted as expected. In the frst half of 2020, as the num- range [1]. However, the epidemiological characteristics ber of reported cases increases in each country (i.e., the of the four countries are diferent. Terefore, to assess gray bars), the negative efect begins to increase accord- the syndemic of COVID-19 and malaria intervention, it ingly. However, because no NPIs are implemented before would be necessary to simulate the various COVID-19 June 17, 2020, the negative efects of diferent NPIs are responses in each country separately. all the same. Terefore, the three lines are overlapped. To simulate the impact of COVID-19 response, in this After June 17, 2020, diferent NPIs are implemented. study, we have conduct scenarios analysis on two groups It can be observed that the more stringent the NPI, the of NPIs: (1) contact restriction and social distancing by R0 more it can achieve the desired goal. When no NPIs reducing to 25% or 10% of its original value while D are implemented, the negative efect is the largest (see keeping I unchanged, and (2) early identifcation and D the red curves in Fig. 4). When R0 is reduced to 25% on isolation of new cases by reducing I to two or four June 17, 2020, the negative efect is alleviated after Sep- days. Te results have shown that the former NPIs can tember. Te reason is that the COVID-19 epidemic can- efectively reduce the scale of the epidemic, while the not be controlled before September. However, when the latter can shorten the duration of the epidemic. In this combined NPI (i.e., R0 is reduced to 25% and DI = 2 ) case, a combination of them should be a better response is implemented, the epidemic can be controlled much to the COVID-19 epidemic. In addition to the type of earlier. Hence, the negative efect is alleviated before NPIs, when to intervene is much more important. Our September, and the negative efects is the smallest (see simulation results have also shown that if the NPIs are turquoise lines in Fig. 4). implemented one month earlier (see Fig. 3), the scale of infection will be greatly reduced, and the epidemic will Discussion be efectively controlled much earlier. Because of this, As the COVID-19 pandemic spreads rapidly in Africa, the early adoption of comprehensive NPIs like China is there is an urgent need to mitigate the negative impact of essential to timely and efective control of COVID-19 the coronavirus in malaria-endemic countries and con- epidemic. tribute towards a syndemic of COVID-19 and malaria Diferent from the COVID-19, malaria is a mosquito- intervention. To achieve this goal, the frst priority is to borne infectious disease, whose transmission depends on assess the epidemiological characteristics of the COVID- various impact factors, such as climate [28, 29], human 19 pandemic in each country. Because populations in movement [30–33], and socio-economic factors [34, 35]. diferent countries have diferent contact patterns, the In this study, we adopt the notion of vectorial capac- epidemiological parameters (e.g., the basic reproduc- ity to represent the transmission potential of a mos- tion number R0 ) of COVID-19 may also vary in diferent quito population in the absence of Plasmodium. Existing Shi et al. Infect Dis Poverty (2021) 10:5 Page 11 of 12

studies have shown that the value of VCAP can be esti- to an underestimation of the severity of the COVID-19 mated based on meteorological factors, such as precipi- epidemic. Nevertheless, in reality, the decisions to reduce tation and temperature [19]. Because diferent countries or suspend the distribution of ITNs are based on the have diferent meteorological conditions, the patterns of reported COVID-19 cases. Terefore, even the severity of VCAP vary greatly through a year (see Fig. 4). In Ethio- the COVID-19 epidemic is underestimated, it may have pia and Nigeria, the high-risk season for malaria trans- little impact on assessing the impact of NPI response on mission is from June to October. In contrast, Zambia has the potential risk of malaria transmission as long as the a relatively low risk of transmission during this period. number of reported cases can be correctly estimated. While in Tanzania, the risk of transmission is relatively In the future, when information about asymptomatic stable throughout a year. infections is available, it would be possible to tackle the To assess the syndemic of COVID-19 and malaria inter- underreporting issue by constructing more precise trans- vention, it would be necessary to consider whether they mission models. spread together over a period of time. On the one hand, if the COVID-19 occurred during the malaria transmis- sion season, it would have a great impact on the alloca- Conclusions tion of resources for malaria prevention and control, In this study, we have presented a data-driven method to thereby greatly reducing the expected efect of malaria assess the syndemic of COVID-19 and malaria interven- intervention. For example, in Ethiopia and Nigeria, even tion in four malaria-endemic countries in Africa: Ethio- with strict prevention and control measures, it is still pia, Nigeria, Tanzania, and Zambia. To achieve this goal, impossible to contain the COVID-19 epidemic before the we have frst estimated the epidemiological parameters, malaria transmission season (from June to October). In i.e., the basic reproduction number R0 and the dura- this case, the negative impact of the COVID-19 epidemic tion of infection DI , based on the time series of reported will be amplifed due to the cumulative efect on unal- COVID-19 cases of each country. Ten, we have simu- located ITNs (see the third column in Fig. 4). To jointly lated COVID-19 epidemic under two groups of NPIs: (1) address endemic malaria and pandemic COVID-19 in contact restriction and social distancing, and (2) early such countries, as suggested by World Health Organiza- identifcation and isolation of cases. Based on the simu- tion, it would be necessary to maintain core malaria con- lated epidemic curves of COVID-19, we have quantifed trol services (e.g., ITN distribution) while implementing the impact of COVID-19 response on the distribution of stringent NPIs for COVID-19. On the other hand, if the ITNs in each country. Finally, we have further assessed simulated peak of the COVID-19 epidemic is not in the the negative efects of COVID-19 pandemic and various malaria transmission season (e.g., Zambia), it will have NPIs on the transmission potential of malaria using the little impact on the expected efects of malaria interven- notion of vectorial capacity. Te results and fndings in tion in a short period. Even so, the cumulative efects still this paper provide a way to jointly address COVID-19 can be amplifed in the future. Terefore, to avoid the and malaria transmission, as well as to efciently utilize resonance of COVID-19 and malaria, the best way is to limited health services in malaria-endemic countries in do everything possible to contain COVID-19 before the Africa. next malaria transmission season. Otherwise, it would be necessary to allocate as many resources as possible for Abbreviations malaria prevention and control before the arrival of the ITN: Insecticide-treated net; PMCMC: Particle Markov chain Monte Carlo; NPI: next malaria transmission season. In summary, in African Non-pharmaceutical intervention; VCAP: Vectorial capacity; SEIR: Susceptible– exposed–infectious–removed; MIS: Malaria indicator survey; PMI: President’s countries where malaria is considered to be epidemic, malaria initiative. strategically maintaining core malaria control services such as the distribution of ITNs, is crucial important Acknowledgements Not applicable. during the COVID-19 pandemic. Limited by the data available at the moment, the pro- Authors’ contributions posed method in this study still has several limitations BS, SX, XNZ and JL designed research, BS, JZ, SX, XW, and YL collected and processed the data, BS, JZ, SX, SL, XW, YL, XNZ, and JL conceived the experi‑ that are worthy of being improved in the future. First, ments, BS, JZ, SL and SX conducted the experiments, BS, JZ, SX, SL, YL, XNZ, without identifying asymptomatic infections, it would and JL analysed the results. BS, JZ, SX, XNZ, and JL wrote the manuscript and be difcult to quantitatively assess the impact of asymp- all authors reviewed the manuscript. tomatic infections on the COVID-19 epidemic in the Funding real world. Second, asymptomatic infections can also This work was supported in part by the Hong Kong Research Grants increase the difculty of case identifcation and result in Council (Grant Nos. RGC/HKBU12201619, RGC/HKBU12201318, and RGC/ HKBU12202220). The funders had no role in study design, data collection and the underreporting of COVID-19 cases. Tis will lead analysis, decision to publish, or preparation of the manuscript. Shi et al. Infect Dis Poverty (2021) 10:5 Page 12 of 12

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