Pan-African Evolution of Within- and Between-Country COVID-19 Dynamics

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Michael Muvawala Joseph ayprso fia ncmiainwt h ag,intergenera- large, the in with (3), capacity combination infections healthcare in Africa, low severe of relatively parts or many the the- symptomatic that (2). could predicts fewer that pandemic modeling distribution to the age of lead younger year oretically a first has the Africa within Although projected are Africa modeling COVID-19 Africa. in a pandemic COVID-19 be the control should equitable to suppression, priority Seeking coordinated infection continental with data. in balanced reported strategies interventions, the country-specific social of were and model quality behavioral the the from numbers by case driven of forecasts transmis- One-week between-country external sion. and on within-country countries local of the spe- status landlocked capacity, and testing temperature, humidity, policies, cific policy Human containment social the of Index, of effect Development the effects observed We lagged history. meteorological the and and variations and throughout socioeconomic geoeconomic country by a parameterized continent, to African external entire or occur- the within We case cases country-level strategies. on the based predict control rences helps on that strategy decisions lives. a for created of pro- information to timely millions critical hetero- are vide threatening forecasts is modeling short-term and pandemic and Surveillance 2020) Africa (COVID-19) 28, December throughout review 2019 for (received geneous disease 2021 25, coronavirus May approved and The NJ, Princeton, University, Princeton Massey, S. 16802 Douglas PA by Park, Edited University University, State Pennsylvania The Physics, of Department and 17033; 02114; PA MA Hershey, Medicine, of College University 17033; Kingdom; 16802; PA United Hershey, 4YW, Medicine, LA1 of Lancaster College University, Lancaster Statistics, and Computing b a Ssentongo Paddy dynamics between-country COVID-19 and within- of evolution Pan-African PNAS (https://africacdc. Africa Prevention the and to all mil- Control org/covid-19/ according in 1 Disease reported States, over for been Member had Centres 2020, (AU) deaths 13, 20,000 Union August over African and By cases 2020. new 14, lion February on Egypt socioeco- policy rapid and inform development. to factors, seeking variables, meteorological demographic and health, nomic move- on public data human and available affecting ment openly policy combine from social and epidemiology, incidence strat- of coronavirus spatiotemporal advantage case a take chose country-level to We egy predict sources. frame- external surveillance and and internal disease track data-driven to a work this policy developed address health To we public allocation. inform need, resource to and required strategies is mitigation data on case interpreta- Time-critical daily time. of predict real tion in and sources track their to and methods cases to new effective efforts are countries’ pandemic African the to control health- Essential fragile (1). and disease infrastructure of care spectrum unique continent’s the by T Index Development Human Whalen J. Andrew etrfrNua niern,Dprmn fEgneigSineadMcais h enyvnaSaeUiest,Uiest ak A16802; PA Park, University University, State Pennsylvania The Mechanics, and Science Engineering of Department Engineering, Neural for Center eateto ulcHat cecs h enyvnaSaeUiest olg fMdcn,Hrhy A17033; PA Hershey, Medicine, of College University State Pennsylvania The Sciences, Health Public of Department h rtCVD1 aeo h otnn a eotdin reported was continent the on case COVID-19 first The nArc straeigmlin flvs rsscompounded crisis a lives, of millions threatening is pandemic Africa (COVID-19) in 2019 disease coronavirus ongoing he 01Vl 1 o 8e2026664118 28 No. 118 Vol. 2021 f ainlPann uhrt,Kmaa Uganda; Kampala, Authority, Planning National j pdmooy isaitc n rvninIsiue nvriyo uih 01Zrc,Sizrad and Switzerland; Zurich, 8001 Zurich, of University Institute, Prevention and Biostatistics Epidemiology, .Oe 4mlincssad1000dah in deaths 190,000 and cases million 44 Over ). | forecast a,b,1 f a,i aa .Nahalamba B. Sarah , a,2 ee .Diggle J. Peter , enadHeld Leonhard , lui Fronterre Claudio , | Africa | meteorology e eateto eerlg n topei cec,TePnslai tt nvriy nvriyPr,PA Park, University University, State Pennsylvania The Science, Atmospheric and Meteorology of Department c,2 j,2 n tvnJ Schiff J. Steven and , i eateto ersrey ascuet eea optl avr eia col Boston, School, Medical Harvard Hospital, General Massachusetts Neurosurgery, of Department f hlpO Omadi O. Philip , c,1 hitpe Jewell Christopher , | g iityo elh apl,Uganda; Kampala, Health, of Ministry nrwGeronimo Andrew , f a,d,k,2,3 enr .Opar T. Bernard , c,2 doi:10.1073/pnas.2026664118/-/DCSupplemental at online information supporting contains article This 3 2 1 OI-9 l tde odt aefcsdo oeig or modeling, on focused have date and to humidity, recent studies temperature, many All between are link COVID-19. there the that unsurprising exploring (MERS-CoV) therefore studies is coronavirus It and syndrome 19). acute (18, (15–17), respiratory severe East (SARS-CoV) (11–14), Middle influenza coronavirus seasonal syndrome from respiratory survival and of Africa. in infrastructure epidemic and COVID-19 oth- the medical and controlling significant in (9), challenges additional Ebola such poses (8), dengue diseases (10) (7), infectious ers tuberculosis of (6), coexistence malaria individ- the as infected Moreover, in COVID-19 (5). severe the of uals risk addition, predicted increased is In to HIV/AIDS lead (1). as to such countries comorbidities of high-income prevalence in high higher seen rates fatality those infection than to lead could (4), households tional See BY-NC-ND) (CC 4.0 NoDerivatives License distributed under is article access open This Submission.y Direct PNAS a is article This interest.y competing no declare authors The ulse ue2,2021. 29, June Published S.J.S. and P.J.D., M.M.N., H.G., A.J.B.M., C.J., L.H., paper. J.M., P.K.M., the A.J.W., S.J.G., wrote A.G., S.A.S., C.F., P.S., B.T.O., and P.O.O., A.G., data; C.F., analyzed P.S., S.B.N., S.J.S. research; and performed P.S., M.M.N., S.J.S. H.G., research; and Y.W., designed S.J.G., M.M.N., S.J.S. H.G., A.J.W., and Y.W., M.M.N., S.J.G., C.J., A.G., C.F., L.H., A.G., C.F., P.S., contributions: Author owo orsodnemyb drse.Eal [email protected] Email: work.y addressed. this be to may equally correspondence contributed whom To S.J.S. and P.J.D., M.M.N., H.G., A.J.B.M., C.J., work.y L.H., this to equally contributed A.G. and C.F., P.S., oto h OI-9pnei nAfrica. in pandemic to COVID-19 priority the continental control a be strategies should suppression country-specific transmission coordinate in COVID- emphasize to complex findings efforts These regional real-time continent. that African upon the act from data and the 19 improves interpret framework to This pandemic gen- ability basis. the and country-by-country of cases, evolution a the of on of dynamics neighbors. prediction the and short-term its social to erate from and contribute capacity or policy testing how country health show a further results within The distinguish from to ability arising the cases obtain provides approach to continent. the challenging of Africa, are African across evolution data the mobility the throughout high-quality understanding Because pandemic for COVID-19 strategy the a created We Significance baa .B Muwanguzi B. J. Abraham , eerlgclvralshv enlne otetransmission the to linked been have variables Meteorological online d,1 d eateto ersrey h enyvnaSaeUniversity State Pennsylvania The Neurosurgery, of Department tvnJ Greybush J. Steven , o eae otn uha Commentaries.y as such content related for y h eateto eiie h enyvnaState Pennsylvania The Medicine, of Department g hmmA Sinnar A. Shamim , https://doi.org/10.1073/pnas.2026664118 . e y c k etefrHat Informatics, Health for Centre etrfrIfciu ies Dynamics, Disease Infectious for Center raieCmosAttribution-NonCommercial- Commons Creative aeaK Mbabazi K. Pamela , f,2 ee Greatrex Helen , . y https://www.pnas.org/lookup/suppl/ h a Wang Yan , e,2 f , , e | , f10 of 1

MEDICAL SCIENCES on identifying a statistical link between meteorological variables 100,000), Morocco (94 cases per 100,000), and Algeria (83 cases against reported COVID-19 cases, without laboratory studies. per 100,000) driving the overall numbers (Fig. 1). As more A recent systematic review reports agreement among published countries conduct targeted mass screening and testing, these fig- research, with cold and dry conditions contributing to COVID- ures continue to change. The spatial distribution of cases per 19 transmission (20). However, these results must be considered 100,000 displays no clear geographical pattern (Fig. 1). South preliminary. Early studies of COVID-19 transmission focused Africa, Djibouti, Equatorial Guinea, Gabon, and Egypt carry the on the emerging pandemic during the boreal spring of 2020 largest burden of cases per capita, ranging from 100 to 500 per (March through May), when the majority of cases were found 100,000. The epidemiological curves for the African countries in China, the United States, and Europe. It is difficult, there- display varying shapes, mostly driven by the frequency and inten- fore, to extrapolate meteorological results to the very different sity of testing. For example, the epidemiological curve of South climates found in the tropics, for example, from the subsequent Africa is similar to those of the United Kingdom and the United outbreaks in India and Brazil. Many low- and middle-income States (SI Appendix, Fig. S1). An exception is Tanzania, which countries, as defined by the World Bank, are located in the trop- stopped reporting new cases in late April of 2020 (SI Appendix, ics, where many potential confounding factors could mimic a Fig. S1). weather signal. These factors include median age, testing and Time series for case incidence and temporally varying model health capabilities, population density, access to sanitation, and inputs are shown for selected countries in Fig. 1A. The full the number of new cases arriving in a country through global set of case incidence time series for all countries can be travel hubs (21). It is also difficult to extract seasonality from a found in SI Appendix, Fig. S1. A majority of the countries single outbreak that has only lasted a single full year, and consid- imposed containment policies, including lockdowns and cur- erable caution has been raised regarding tropical caseload and fews, in early March 2020 to prevent further COVID-19 trans- confounding factors (22). mission within their borders. These social policy interventions The human response to the pandemic can also drastically remained in effect through August 2020 for most countries (SI shape its timing and intensity. Where data on social distanc- Appendix, Fig. S2). Testing policies, which were restrictive at ing are sparse, government testing and stringency policies (see the beginning of the pandemic due to inadequate testing infras- Materials and Methods) can be used as a common surrogate to tructure, have become more open as testing is made widely compare countries’ efforts to contain the spread of the virus, available (SI Appendix, Fig. S3). As expected, spatiotempo- bolster healthcare systems, enact rigorous testing policy, and pro- ral distribution of temperatures, rainfall, and specific humid- vide economic support. The Oxford Coronavirus Government ity are very heterogeneous across the continent (Fig. 1 and Tracker (OxCGRT) standardizes these complex systems into a SI Appendix, Figs. S4–S6). Population-weighted averages of these set of policy metrics in each of these domains (23). More strict three meteorological variables were calculated for each country social policies identified in the OxCGRT have been associated and day. This type of weighting prioritizes the human–climate with reductions in human mobility (24, 25). Across 161 countries, interaction over the land–climate interaction (SI Appendix, some of these policies were significantly associated with lower Figs. S7–S9). per capita mortality (26), including school closing, canceling public events, and restrictions on gatherings and international travel. Optimal Model. The spatiotemporal dynamics of reported cases Likewise, others have found that strict policies are negatively are modeled as additive components. Two epidemic sources cap- associated with the growth of new cases (27–29). The relation- ture infections coming from within the country and from all ship between policy and observed changes in social distancing, other countries. An endemic component includes all contribu- case numbers, and mortality is complicated by an unknown delay tions to the reported number of cases that are not taken into of effect. One estimate indicates a decline in growth of new account by the epidemic part. The endemic part is not driven by cases within 1 wk of enacting strict policy, and deceleration of previous case counts but may account for factors such as sea- growth within 2 wk (29). Although the implementation of con- sonality, sociodemography, animal reservoirs, and population. tainment policies can be and has been used by many African The epidemic part of the model has an autoregressive nature, nations (30), lockdown cannot be maintained in these coun- meaning that the past number of COVID-19 cases reported both tries without a worsening of severe poverty and resultant loss within a specific country and in the rest of the continent will of life (31, 32). be used to forecast the trend of COVID-19 cases. How much We have therefore developed a COVID-19 surveillance, mod- the past observations contribute to the future disease count eling, and prediction strategy that explores a growing spatiotem- depends on two parameters, λ for the local transmission and poral database on coronavirus epidemiology, meteorology, and φ for the external transmission, and will be estimated from the social policy interventions. To model the spread of COVID-19 data. In particular, the impact of cases reported in the neigh- in Africa, we employ a data-driven endemic–epidemic model boring countries depends also on a set of weights that modulate (33) to 1) visualize the burden of cases, including the pro- the spatial connectivity of the countries in the continent (see portion of cases arising from sources local within country and Materials and Methods). These two parameters are also func- external between country, 2)describe the factors which most cor- tions of social policy, testing availability, and meteorological relate with spread, and 3) enable short-term forecasting of new and demographic factors whose association with transmission we cases. This modeling framework has been used previously to fit aim to determine. The model specification reported in Eqs. 3–5 space–time dynamics of COVID-19 in Italy (34), Germany (35), representing the endemic, within-, and between-country com- and the United Kingdom (36) and to analyze other infectious ponent of the model, respectively, is the result of a model diseases (37). selection procedure based on the Akaike Information Criterion (AIC) (38). A summary of model comparison and selection pro- Results cess is presented in SI Appendix, Table S1. We began with an COVID-19 Spread and Response. As of August 13, 2020, the 55 AU intercept-only model (model 1) with a population offset in the Member States had reported over 1,000,000 cases and 20,000 endemic component of the model and country’s measure of con- deaths from COVID-19. The southern region had the most nectivity based on a power law. More complicated versions of cases, reporting over 50% (over 560,000 cases and 11,000 deaths) the epidemic component were evaluated by sequentially adding of the total for the continent. North Africa carries the high- weather, demographic, stringency index, and testing policy in the est regional case fatality rate (4%) but contributes 20% of the formula for the within- (λ) and between-country (φ) components continent’s cases, with countries such as Egypt (102 cases per of the model. We also tested whether multiple lags for cases and

2 of 10 | PNAS Ssentongo et al. https://doi.org/10.1073/pnas.2026664118 Pan-African evolution of within- and between-country COVID-19 dynamics Downloaded by guest on September 27, 2021 Downloaded by guest on September 27, 2021 re oe ihPisnadgoerclgwihsrevealed in weights improvement largest lag (∆ the AIC model, geometric first-order the and to relative Poisson that, with model considering order patterns: observed respectively. the validation, lags model described for out better held data covariates the and model the fit 2020. to 13, used August countries data shown on of representative model 100,000 window for best-fitting per time the humidity cases the in cumulative specific show variables) of areas and (explanatory distribution shaded predictors rainfall, Country-specific orange as temperature, (B) and used policy, gray were testing covariates The time-dependent 1. index, Table These stringency in Uganda. 100,000, and Africa, per South cases Senegal, reported Egypt, daily bottom) to top 1. Fig. a-fia vlto fwti-adbtencutyCVD1 dynamics COVID-19 between-country and within- of evolution Pan-African al. et Ssentongo (P contribu- transmission higher within-country 11% the in increase to results SD tion temperature 1 mean a lagged example, negative For the a cases. in of had transmission humidity the on specific effect higher but factor, contributing parameters. transmission the to covariates time-varying cases. Landlocked of con- S11 1. transmission significant Fig. local Table were the for policy in factors and testing reported tributing and model risks are index, final relative stringency CIs this status, The 95% in effects. infections included associated random of variable the as sources explanatory country (5) each each for neighbor-driven for and of vary (4) levels to local (mean intercept the the in allowed observed by we explained trans- not covariates, country-specific levels capture incidence and better dynamics to mission and Africa across cases effects; random without 5 model S1). in Table 48,437 Appendix, (from to SI AIC 1 lowest model the in yielded 48,824 component, policy testing within-country and fac- the index, in meteorology stringency status, with landlocked and population HDI, tors, component, and between-country status, the landlocked in (HDI), Human including with index Index model stringency policy, Development the testing Therefore, cases, for S10). d 7 Fig. lag Appendix, (SI variables all 10 15 20 10 15 20 10 20 30 10 20 30 25 50 75 A 0 5 0 5 0 0 0 2 4 6 0 nadto,hge agdma eprtr a positive a was temperature mean lagged higher addition, In reported of heterogeneity spatiotemporal high the to Due a 9Mr2 p 6Ar2 a 4My1 u 1Jn1 u 9Jl1 u 7Aug 10 Jul 27 Jul 13 Jun 29 Jun 15 Jun 01 May 18 May 04 Apr20 Apr06 Mar23 Mar 09 d 1, = iesre Fbur oAgs 00 f(from of 2020) August to (February series Time (A) factors. weather and policy, testing index, stringency cases, reported of distribution Temporal I = AIC hw h culcnrbto ftetm-osatand time-constant the of contribution actual the shows . . . , ,6)wsahee ihamdlwith model a with achieved was −1,560) where D, gp eea ot fiaUganda SouthAfrica Senegal Egypt D 4dy.Epoaino h higher- the of Exploration days. 14 = Day .2,rltv risk relative 0.023, = IAppendix, SI λ D

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Temperature C g g Humidity k R i mm ain ae e 100000 per Cases e x Ind Stringency esting T φ) B 159 97 69 56 35 24 18 10 5 0 otnna-ee en noa hd eoh,Nmba and Namibia, the Lesotho, than signif- Chad, transmission had Angola, between-country Senegal mean. of continental-level and rates Congo, higher of icantly Republic Ghana, Malawi, Gabon, Ethiopia, Guinea, Benin, Republic, the cases, African signifi- of is Central Cameroon, transmission rate Congo the to to transmission of respect contributions With between-country mean. within-country Republic continental-level the the higher a than lower hand, significantly cantly with other effect country the an are only On with Djibouti one. countries and than African Africa of only South number the past transmission, the within-country the to given the to of contributions cases respect fewer With rest individuals. or infected the reported more propen- than country-specific generate average a to an (lower) as sity interpreted has higher be country may is This a continent. that that value means rate A one transmission transmission. than to (lower) contributions higher unexplained capture to unexplained the as of 1.78) part to absorbed countries. 1.63 effects between CI random variability 1.87 95% the CI (1.70, that model 95% sign effects (1.95, a random model to effects 2.03) fixed to the from countries decreased neighboring (P contri- eter from twofold the cases by four, higher of to was transmission zero from the policy to testing bution the in increase of level openness each the With remained countries. neighboring of testing the numbers from of the cases explaining accessibility factor contributing Greater significant 0.94). only the to in con- 0.78 (P increase lower cases CI 14% SD of a 95% transmission 1 in local a resulted the humidity However, on specific tribution 1.21). mean to lag 1.01 7-d the CI 95% 1.11, [RR] − − − − − − − − − 10 5 − 159 97 69 56 35 24 18 i.2soscutyseicrno fet,wihw used we which effects, random country-specific shows 2 Fig. 960 Total per100,000upto08 casesreported < .01.Teoedseso param- overdispersion The 0.0001). https://doi.org/10.1073/pnas.2026664118 0 0010 0020 km 2500 2000 1500 1000 500 0 − .0,R 0.86, RR 0.001, = 13 − PNAS 2020 | f10 of 3 N

MEDICAL SCIENCES Table 1. Maximum likelihood estimates and corresponding 95% case count data are captured well within model prediction inter- CIs for a model with a 7-d lag vals (Fig. 4 and SI Appendix, Fig. S13). Across countries, the Final model model predictive performance was assessed with a calibration test based on proper scoring rules as described in ref. 39. A map Parameter Relative risk 95% CI P value of P values for the calibration test is shown in SI Appendix, Fig. Endemic S14. Overall, model predictions are well calibrated, and a mis- Intercept 11.071 (7.150, 17.142) — alignment between forecast and observations was only detected for a few countries (p < 0.05, Burundi, Cameroon, Somalia, and Within-country Botswana). Intercept 0.958 (0.550, 1.669) — Discussion log(population) 1.036 (0.956, 1.123) 0.391 We present a COVID-19 surveillance strategy that can improve HDI 0.957 (0.819, 1.118) 0.580 the ability of African countries to interpret the complex data Landlocked 0.674 (0.531, 0.857) 0.001 available to them during the pandemic. This approach balances Stringency 1.872 (1.170, 3.000) 0.008 t−7 the simplicity and consequent robustness of an empirical model Testing 0.817 (0.729, 0.918) 0.001 t−7 against the more complex, potentially more realistic but also Raint−7 1.045 (0.981, 1.112) 0.175 more strongly assumption-driven kind of compartmental mech- Temperature 1.106 (1.014, 1.206) 0.023 t−7 anistic model (40). A key feature of our approach is the ability Humidity 0.856 (0.780, 0.940) 0.001 t−7 to distinguish between case incidence arising from the local within- or neighbor-driven transmission of infection. Distinguish- Between-country ing within- and between-country transmission of cases allows Intercept 0.045 (0.004, 0.481) — us to identify potential strategies for social or health policy log(population) 1.428 (0.923, 2.210) 0.110 intervention. The model further enables reproducing the his- HDI 1.239 (0.584, 2.628) 0.576 tory of the epidemic in relationship to past policy, and producing Landlocked 0.844 (0.306, 2.323) 0.742 short-term predictions of the dynamic evolution of the epidemic. Stringency 1.630 (0.560, 4.749) 0.380 t−7 Due to evolving global policy, complex weather phenomena, Testing 2.322 (1.676, 3.218) <0.0001 t−7 and changes to social behavior, these factors will change their relationship to COVID-19 prediction as the pandemic evolves. ρ 2.186 (1.532, 2.839) — We find that a country’s testing capacity, social policy, land- ψ 1.703 (1.631, 1.775) — locked status, temperature, and humidity are important con- For climatic variables, a 1 SD increase in climatic variables results in the tributing factors explaining the within- and between-country shown relative risk. For stringency index, a 10% increase in stringency is transmission of cases over the window of analysis. The availabil- associated with the increased relative risk shown. HDI and testing policy are ity of more testing to a wider swath of the populace is a potent on ordinal scales zero to three (HDI) and zero to four (testing policy). Bolded contributor to reduced case transmission within country, while estimates are statistically significant. The spatial weight decay, ρ, reflects having the opposite effect on case transmission from neighboring the strength of intercountry connectivity, and ψ, reflects the overdispersion countries. Testing policy, another surrogate for healthcare capa- parameter. bility and preparedness to handle the pandemic, demonstrates this unique opposing effect on the two model components. Coun- Tanzania had lower between-country transmission rates com- tries in northern and southern Africa that have relatively high pared to the continental-level mean. The estimated variation of HDI demonstrated comparatively higher numbers of cases per these country-specific effects in the within-country component of population. On the other hand, even in the face of border clo- 2 the model is small (σˆλ = 0.07) compared with their variation in sures, landlocked countries depend on open borders for trade. 2 the neighborhood component (σˆφ = 2.3). Although the between- For such countries, strict border closure measures are diffi- country variability of transmission resulting from cases reported cult to impose, resulting in a continual influx of cases from the outside of the country was larger, the between-country intercept neighboring countries. (Table 1) is very small, and so the neighborhood component is, The observed association of temperature and specific humidity in general, a small contributor to the fit. with the case numbers, although small, points to the possible bio- logical and behavioral responses to weather patterns, which, in Contributions of Within- and Between-Country Transmission. We turn, drive the dynamics of SARS-CoV-2 infection. Temperature distinguish between endemic, within-country, and between- and humidity are known factors in SARS-CoV, MERS-CoV, country contributions to the mean number of cases. Fitted and influenza virus survival (41–43). Lower humidity has been values for all components according to model formulations consistently associated with a higher number of cases. Besides in Eq. 1 are shown in Fig. 3, with a complete listing in SI potentially prolonging half-life and viability of the virus, other Appendix, Fig. S12. The number of cases attributed to within- and potential mechanisms associated with low humidity include sta- between-country transmission of cases during the entire study bilization of the aerosol droplet, enhanced propagation in nasal period varied greatly. Across countries, the contribution from mucosa, and impaired localized innate immunity (44). Whether the endemic component was found to be minimal. Of the 46 the observed association is driven by the change in social behav- countries analyzed, 16 of them are landlocked, and 13 (81%) of ioral patterns or the effect on the survival of SARS-CoV-2 these had a substantial contribution of cases from their neighbor- remains to be explored (45). It is also possible that the observed ing countries: Botswana, Burkina Faso, Burundi, Central African contribution of meteorological factors to case transmission might Republic, Ethiopia, Lesotho, Malawi, Rwanda, South Sudan, be an artifact of spatial averaging and assigning one meteorolog- Swaziland, Uganda, Zambia, and Zimbabwe. ical value to an entire country. It will be important to explore such associations in more detail before any policy-relevant con- Short-Term Forecast. We keep the last 7 d of data out of the clusions can be drawn. Thus, at present, policy makers must focus fitting procedure in order to use them as a forecast validation on social–behavioral interventions such as reducing physical dataset. We produce 1-wk-ahead predictions and compare them contact within communities and vaccination, while COVID-19 with the reported data to check the quality of the model fore- risk predictions based on climate information alone should be cast. The results show that the majority of individual country interpreted with caution (46).

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MEDICAL SCIENCES A B

Fig. 3. Contributions of new cases from endemic, within-country, and between-country model components. (A) Black dots and connecting lines are the observations, and shaded colors are the model predictions, from the three contributing components of the model. (B) The relative contribution of new cases from within-country sources, by country. Contribution plots for all countries are given in SI Appendix, Fig. S12.

against high infection rates and mortality until effective vac- infectiousness (48, 50), and latency (48) of COVID-19 transmis- cines are widely available. However, it is anticipated that physical sion, by extending the observational interval of the infectious distancing measures enforced to limit transmission will also process to several days. The Poisson autoregressive weighting restrict access to essential non–COVID-19 healthcare services, method used in our modeling strategy also captures an initial such as disruptions in the existing programs for tuberculosis, increase in infectiousness and may thus be more appropriate for HIV/AIDS, malaria, and vaccine-preventable diseases, causing longer serial intervals or daily data. In their recent work, Bracher long-lasting collateral damage on the continent (32). Although and Held (51) show that moving beyond 1-d lags to higher-order between 29 million to 44 million individuals (2) in Africa were time lags improves predictive performance of these endemic– projected to become infected in the first year of the pandemic epidemic models. For our optimization scheme, we tested lags if containment measures fail, these numbers may be under- up to 14 d, and found that a lag at 7 d provided the best model estimates, since the proportion of asymptomatic infections is fit. Short-term predictions enable the monitoring of case inci- not well established. Since detection is biased toward clinically dence trends but are limited by high levels of uncertainty. This severe disease, the attack rate of the infection is probably sub- is the result of the nonnegligible overdispersion detected in the stantially higher than what is reported. At the beginning of data and due to the several sources of unmodeled spatial and the pandemic, it was estimated that up to 86% of all infec- temporal heterogeneity across the continent. tions were undocumented and were the source of 79% of the documented cases (47). Such observations explain the rapid geo- Limitations. A number of assumptions in our analysis reflect graphic spread of the infections and challenging efforts at con- practical limitations. We used aggregated data at the coun- tainment. The number of asymptomatic cases is best determined try level to ensure continental coverage, but fitting this model by population-based seroepidemiology data. However, due to at different spatial scales would require reformulation of the the fragile healthcare systems of African countries, this type of neighborhood structure and the model parameters. It is difficult disease surveillance remains limited. On the other hand, it is also to obtain accurate mobility data within and between countries plausible that the lower incidence rate of the virus in Africa is throughout Africa, necessitating an indirect estimation of con- because of the investment in preparedness and response efforts tact probabilities. Although our use of higher-order neighbor- toward various outbreaks on the continent (such as Ebola virus hood (beyond sharing a border) contact patterns led to improved disease, Lassa fever, polio, measles, tuberculosis, and HIV) (32). model fit compared to an assumption of first-order neighbors This technical know-how has been swiftly adapted to COVID-19. (bordering countries) only, more-accurate mobility data would An additional strength of our modeling strategy is the abil- improve these estimations. Because ground truth data on within- ity to incorporate the disease-specific serial interval between country vs. imported cases are not available, we cannot validate sequential infections in the autoregressive model. We attempted case origins in our model fits. Other assumptions related to to mimic the longer (greater than 1 d) serial interval (48, 49), the testing and stringency policies are coarse approximations of

6 of 10 | PNAS Ssentongo et al. https://doi.org/10.1073/pnas.2026664118 Pan-African evolution of within- and between-country COVID-19 dynamics Downloaded by guest on September 27, 2021 Downloaded by guest on September 27, 2021 a-fia vlto fwti-adbtencutyCVD1 dynamics COVID-19 between-country and within- of evolution Pan-African al. et Ssentongo etacutfrteeiei yaiswti n between and within dynamics that epidemic relationships the changing for for the refit virus establish account be the to should best of periods model variants this time the infectivity, later an as in during and changes time evolve, introduce African of policies the window As and properties. any pandemic contributing on the modeling examine focus to Our to epidemic consider. us to enables important strategy become and will vac- observations against predictions, effective modification validation and Africa, As framework, within model model. this available of the widely in more dynamics become cines nonlinear of lack the improve the seasons may reservoirs, beyond dry animal fit. extends or and model oscillations term, pandemic seasonal rainy endemic the the as to or such covariates As additional winter, introducing is equator. year, and first weather the with summer to viral interaction in closer with the same sea- that changes ignored the implying further infectivity variation, We and as vaccination. sonal data and change case reinfection, empirical on not variants, only The embrace does depends pandemic. to employed therefore evolving need we data the the approach sufficient versus model of of fitting nonstationarity trade-off model a the stable represents for This required time. over (52). stant distribution interval serial underlying estimation the the of confound weights lag may of and and in the structure asymptomatic artifacts autocorrelation introduce In disentangle the can not pathogen. Underreporting disease. could the symptomatic we transmit analysis, unknowingly present who individuals convergence. model prevented strin- which S (Madagascar, Verde, missing nations Cape island Pr to Seychelles, six the Mauritius, due Guinea, excluded Comoros, Equatorial Sahara, and excluded index, Western gency We capita and approaches. per dis- Guinea-Bissau, tests such quantifiable of all of control limits absence and The The mean. surveillance case transmission. forecast the ease predicted to see the countries, is response of list line governmental full orange the solid For retrospective the intervals. predicted prediction and the model is forecast, within line model blue well solid during captured the are cases and data represent cases, count observed area case are country shaded shade individual orange blue of the the majority in in circles circles Filled respectively. Open colors, light mean. and dark by represented are 4. Fig. ´ nie,det h ako onciiyt anadAfrica mainland to connectivity of lack the to due ıncipe), hsmdln taeyi iie yteqaiyo h aaand data the of quality the by limited is strategy modeling This con- were coefficients model that assumed we Additionally, healthy-appearing otherwise by carried often is SARS-CoV-2 ersetv n oeatmdlfi o eetdcutis ersetv oe t(lesae n oeat(rnesae.Te5%ad9%CIs 95% and 50% The shade). (orange forecast and shade) (blue fit model Retrospective countries. selected for fit model forecast and Retrospective ˜ oTom ao ´ ,and e, lhuh ndfeetrgoso h ol,tasiso a ekduring peak may transmission world, humidity, the specific of and regions rainfall, different temperature, in of although, cycles with associated been be to Factors. variables Meteorology/Weather hidden interac- without space the and on time focuses in approach counts reported estimated. cases case modeling autoregressive of in a data-driven tion variability simple Such relatively this approach. a of is of modeling variability It choice our substantial countries. motivates across in that counts given results case challenges, heterogeneity reported poses this between continent Indeed, heterogeneity environmental African countries. and political, the cultural, over extensive the COVID-19 of spread aaa u otemsigdt nsrnec ne,o h i island six S the Verde, or Cape index, stringency Seychelles, on Mauritius, data Tom Comoros, missing Western (Madagascar, the and nations Guinea-Bissau, to Guinea, due Equatorial Sahara, for estimates provide not Overview. Methods and Materials COVID-19 the control to Africa. priority in continental pandemic a be should suppres- infection sion, in balanced strategies country-specific interventions, in coordinated cases. social with report advantages and and behavioral test potential equitable to the Seeking resources coordinate to to chal- efforts point especially regional is challenges pandemic Such the track lenging. effectively to the economies, capacity lower-HDI upon of the vulnerability health dependent the fragile with with is settings coupled In systems, accuracy data. short- reported its and for testing used of cases, quality be of can predictions strategy our term Although at infection scale. SARS-CoV-2 national geode- the the of spread sociodemographic, sources. the influence the that within-country meteorological factors into and and mitigation/containment, COVID-19 insight testing, between- of mographic, give forecast quantify analyses short-term to Our perform and and cases track to tool Conclusions. interest. of window time future refit or to past others any enables for code open-source archived Our countries. ,adPr and e, ´ u nlssicue 6cutiso anadArc.W do We Africa. mainland of countries 46 included analyses Our nie,det h ako pta onciiy oeigthe Modeling connectivity. spatial of lack the to due ´ ıncipe), epeetapnArcnCVD1 surveillance COVID-19 pan-African a present We h esnlt fiflez rnmsinhas transmission influenza of seasonality The https://doi.org/10.1073/pnas.2026664118 i.S13. Fig. Appendix, SI PNAS | f10 of 7 ao ˜

MEDICAL SCIENCES the “cold-dry” season (temperate climates) or during “humid-rainy” season Conditional on past observations Yi,t−d, i = 1, ... , N, and d = 1, ... , D, (tropical climates) (53). new COVID-19 cases Yit from country i at time t are assumed to follow a We estimated the influence of meteorological factors on the transmission negative binomial distribution with mean µit and overdispersion parameter dynamics of COVID-19 in Africa. Real-time, daily, in situ synoptic weather ψ as observations are sparse across much of Africa. Therefore, daily, 10-km spa- [Y | Y − , ... , Y − ] ∼ NegBin(µ , ψ). tial resolution mean temperature, rainfall, and specific humidity data were it t 1 t D it obtained from UK Met-Office numerical weather prediction model output 2 The conditional variance is µit + ψµit , which demonstrates the role of the (54, 55). These data are extracted from the early time steps of the model overdispersion parameter to capture variability greater than the mean. The following data assimilation, to more closely approximate an observational conditional mean µit is decomposed into three additive components, dataset. This approach also has the advantage that future studies have

access to the same coherent dataset at a global scale for applications out- D D side of continental Africa. The weather product that generates these data X X X µit = i + λit udYi,t−d + φit udwjiYj,t−d, [1] closely approximates an observational dataset at locations that have dense d=1 d=1 j6=i observation coverage, whereas, in observation-sparse areas, the dataset relies more heavily upon the numerical weather prediction model (a physics- where i, λit , and φit represent three contributions to case incidence. The based rather than statistical model). The meteorological dataset contained ﬁrst term, it , is the so-called endemic component and captures infections no missing data. arising from sources other than past observed cases (e.g., contributions from A population density-weighted spatial average ( , Figs. S7–S9) SI Appendix areas that are not included in the neighbor set). The two other terms in [1], was then applied for each day and country using the R package “exactex- λit and φit , constitute the epidemic part of the model and modulate how tractr” (56). Population density was obtained from the Gridded Population infective individuals reported in the past d days both locally and from neigh- of the World version 4 (GPWv4) from the Centre for International Earth Sci- boring countries will contribute to the average future number of reported ence Information Network (57). Weighting climate variables by population cases. The strength of connection between countries is described by spatial gives a closer approximation to the weather conditions faced by humans weights wji. This intercountry transmission susceptibility is deﬁned using a living in that country compared to an unweighted average over total land power-law formulation proposed by Meyer and Held (66), area. For example, the country of Algeria, in which much of the population resides along the coast, demonstrates a cooler, wetter, and more humid w = o−ρ, [2] climate when weighting by population (SI Appendix, Figs. S7E, S8E, and S9E). ji ji

Stringency Index and Testing Policy. To include an aggregate measure of where oji is the path distance between countries j and i (with oii = 0, oji = countries’ social policies, the stringency index sourced from the OxCGRT 1 for direct neighbors i and j and so on), and ρ is a decay parameter to dataset was used. This composite measure reflects government policies be estimated from the data. The path distance oji is on an ordinal scale related to school and workplace closures, restrictions on public gatherings, based upon the adjacency index. The spatial weights are normalized such P events, public transportation, limitations of local and global travel, stay- that k wjk = 1 for all rows j of the weight matrix (SI Appendix, Fig. S17). at-home orders, and public education campaigns (23). A full description of The normalized autoregressive weights ud are shared between the local these variables is provided in SI Appendix, Table S2. The stringency index is and global epidemic components, and represent the probability for a serial calculated from these categorical variables using a weighted average, with interval of up to D days—which is the average time in days between a range of 0 to 100 indicating weak to strict stringency measures, respec- symptom onset in an infectious individual (or primary case) and symptoms tively. A time-dependent metric of testing policy was also extracted from appearing in a newly infected individual (or secondary case) when both are this dataset. Ranging from zero to four, this categorical metric increases in close contact (67). with more open and comprehensive testing policy. The parameters it , λit , and φit are constrained to be nonnegative and modeled as the natural log-transformed linear combination of different HDI, Demography, United Nations Geographic Regions, and Coastline Access. country-specific covariates. The endemic component, In our modeling strategy, we incorporate key socioeconomic and sociode- () mographic epidemiological data, including HDI, population, United Nations log(it ) = α + log(Ni), [3] geographic regions, and coastline access (SI Appendix, Fig. S15). HDI represents the national data on key aspects of development, namely, education, is decomposed as a constant α() specific to the baseline endemic and a term economy, and health (58). The HDI is the geometric mean of normalized proportional to the country-level population N . In the epidemic part of the indices for each of the three dimensions. The education dimension is mea- i model, we expect new cases to also be driven by country-specific factors: sured by average years of schooling for adults aged 25 y and more and Population (N ), HDI classifications of low, medium, or high (HDI = {0, 1, 2}), expected years of schooling for children of school-entering age. The econ- i i and land-locked (LL ) status for each country are assumed constant over the omy dimension is measured by gross national income per capita, and the i time scale of analysis. Other forces driving new cases vary over time, as a health dimension is assessed by life expectancy at birth. In Africa, the major- response to either policy changes or natural fluctuation in environmental ity of the countries fall in the low-HDI category (SI Appendix, Fig. S15). or societal patterns. Time-dependent covariates include mean daily tem- The northern part of Africa and South Africa have a considerably higher perature (T , −τ ), rainfall (R , −τ ), specific humidity (H , −τ ), testing policy HDI compared to the rest of the continent. Country-specific median age i t i t i t (Xi,t−τ ), and government stringency index (Si,t−τ ), lagged at τ days. The was correlated with HDI (Pearson’s correlation coefficient R = 0.71, P < full set of explanatory variables that contribute to the model from both 0.0001; SI Appendix, Fig. S16); therefore, we excluded this covariate from internal and external epidemic components are formalized in [4] and [5] as the model. We include in the model the 2020 population obtained from

the Population Division of the Department of Economic and Social Affairs (λ) (λ) (λ) (λ) of the United Nations Secretariat (59). The categorization of sub-Saharan log(λit ) = αi + β log(Ni) + γ HDIi + δ LLi + and northern Africa was based on the United Nations geoscheme for Africa (λ) (λ) σ Si,t−τ + χ Xi,t−τ + (60). This regional factor captures the human genetics (61), environment (λ) (λ) (λ) and climate (62), and sociocultural and sociodemographic variations of the θ Ti,t−τ + ω Ri,t−τ + ν Hi,t−τ [4] African population (63). Finally, lack of direct access to the coastline may inﬂuence the ﬂow of infections from neighboring countries, as border trade and remains an essential operation. For example, Uganda introduced border closures and tighter preventive measures on truck drivers’ movements during (φ) (φ) (φ) (φ) log(φit ) = α + β log(Ni) + γ HDIi + δ LLi + the epidemic; despite this, a substantial number of new infections have been i (φ) (φ) imported from truck drivers crossing the border for trade (64). Such cross- σ Si,t−τ + χ Xi,t−τ , [5] border commerce remains a crucial part of the supply chain for landlocked African countries such as Uganda and Rwanda. (λ) (λ) 2 (φ) (φ) 2 where αi ≈ N(α0 , σλ) and αi ≈ N(α0 , σφ) are a set of independent Model Formulation. We chose a class of multivariate time series models for country-level random effects. This modeling framework is implemented in case count data introduced by Held et al. (65), and further extended by the R package “surveillance” (68). A complete table of data sources for Bracher and Held (51) with the addition of higher-order distributed lags. model input is found in SI Appendix, Table S3.

8 of 10 | PNAS Ssentongo et al. https://doi.org/10.1073/pnas.2026664118 Pan-African evolution of within- and between-country COVID-19 dynamics Downloaded by guest on September 27, 2021 Downloaded by guest on September 27, 2021 4 .Saa,M on bouehmdt ouae nunasria,transmission, survival, influenza modulates humidity Absolute Kohn, M. Shaman, J. 14. Africa central The frontier: final COVID-19’s Ebrahim, H. S. Doumbia, S. Ditekemena, J. 10. 7 .B,J ag .E .Wahr rvn oc eidtetasiso fsvr acute severe of transmission the behind force Driving Weather, H. E. J. Wang, the J. on Bi, P. influence meteorological 17. Possible Yu, S. T. I. Yeung, H. K. Chang, L. W. Yip, C. 16. Yuan J. 15. humidity Absolute Lipsitch, M. Grenfell, T. T. B. 13. Viboud, C. Pitzer, E. V. Shaman, J. 12. mortal- influenza and temperature, humidity, Absolute Shimshack, P. J. Barreca, I. A. 11. 3 .Hale that T. Evidence Bannister-Tyrrell, 23. COVID- M. Cameron, Managing R. A. See, Faverjon, C. C. K. Sadler, R. Muchtar, Meyer, F. A. Shrestha, 22. R. B. Liew, F. M. Siow, and temperature T. W. of Effects 21. Normando, D. Vallinoto, R. C. respiratory A. East Bastos, Middle R. of Mecenas, incidence P. and 20. factors Climate Ahmed, E. A. Altamimi, A. 19. Gardner G. E. 18. a-fia vlto fwti-adbtencutyCVD1 dynamics COVID-19 between-country and within- of evolution Pan-African al. et Ssentongo o fet,w sdpoe crn ue o on aa(0.Soigrules Scoring (70). data count ran- for included rules functions that scoring are models proper compare used To we S1). effects, Table dom Appendix, (SI present not Fitting. 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W. effects Jeon, The Seto, S. H. Casanova, W. disease M. Yuen, Y. L. K. infectious Poon, 42. M. of L. L. modelling Lam, Y. S. Multivariate Peiris, M. Toschke, S. J. M. Chan, A. K.-H. 41. Held, L. data. Paul, count for M. tests Calibration identification. 40. Held, L. model Wei, statistical W. the 39. at look new A Akaike, H. 38. Bauer C. 37. Fronterre C. covid-19 36. in used framework Endemic-epidemic Held, L. Dunbar, Bekker-Nielsen M. 35. difficult faces Africa MacPherson, P. Corbett, L. E. Ndeketa, L. Burke, M. R. Divala, T. 31. Gaye B. 30. Utsunomiya T. Y. effective-29. in heterogeneity Cross-country COVID-19: Lorca, M. world- Dechter, E. with Castex, associated G. factors 28. of power explanatory the Ranking Moustakas, A. 27. Leffler T. C. 26. 4 .M iko,G sa .Guin,F at,L aaoi sesn h feto con- of effect the Assessing Savadori, L. Santi, F. Giuliani, D. Espa, G. Dickson, M. M. 34. H M. Held, L. Meyer, S. 33. Loemb M. M. 32. 4 .H .B usi,Src oiyitretosices oildsacn dur- distancing social increase interventions policy Strict Hussain, B. M. H. A. 24. edFn rmteIsiuefrCmuainladDt cecsadthe Virus and Award support. University. Covid Sciences State Data Transformative the and Pennsylvania and The Director’s by Computational advice at for part, Institute NIH Institute their Huck in the US from supported, for a Fund was team lab Seed by M.M.N. the Informatics supported (S.J.S.). thank also Office 1R01AI145057 was We and Met work discussions. data, helpful UK This testing for the Ferrari Uganda M. from providing and for Challan Authority R. Planning National the time future and ACKNOWLEDGMENTS. past examine to users github.com/Schiff-Lab/COVID19-HHH4-Africa. in enable interest, and of paper windows this in results validation fore- the Availability. country’s of Data day each predictive each for for the generated obtained better log-score were the the week. scores averaging score, Mean by the cast, with 73). smaller model (72, The fit autoregressive quality model score. higher-order the the logarithmic both of assess the We forecast estimates. 1-wk-ahead forward parameter carrying and the without in model fitted uncertainty the the from forecast forecasts: plug-in Predictions. Model hie nrsodn oCOVID-19. to responding in choices (2020). vdne https://harris.uchicago.edu/files/orellanamartelo evidence. a.Hm Behav. Hum. Nat. infection. (2019). influenza against tance humidity. on surfaces. on survival coronavirus on Microbiol. humidity relative and perature coronavirus. 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