A study of COVID-19 data from African countries
Kétévi A. Assamagan, Somiéalo Azote, Simon H. Connell, Cyrille E. Haliya, Toivo S. Mabote, Kondwani C. C. Mwale, Ebode F. Onyie, George Zimba
AA study of COVID-19 data from African countries
K´et´evi A. Assamagan a, ∗ , Somi´ealo Azote b, ∗ , Simon H. Connell c , Cyrille E.Haliya d , Toivo S. Mabote e , Kondwani C. C. Mwale f , Ebode F. Onyie g , GeorgeZimba h a Brookhaven National Laboratory, Physics Department, Upton, New York, USA b Universit´e de Lom´e, D´epartement de Physique, Lom´e, Togo c University of Johannesburg, Johannesburg, South Africa d University of Abomey-Calavi, International Chair in Mathematical Physics andApplications, Cotonou, Benin e Universidade Eduardo Mondlane, Grupo de Astrofsica e Ciˆencias Espaciais, Maputo,Mozambique f University of Rwanda, African Center of Excellence for Innovative Teaching and LearningMathematics and Science, Kigali, Rwanda g University of Yaounde I, Department of Physics,Yaounde, Cameroon h University of Jyv¨askyl¨a, Department of Physics, Jyv¨askyl¨a, Finland
Abstract
COVID-19 is a new pandemic disease that is affecting almost every country witha negative impact on social life and economic activities. The number of infectedand deceased patients continues to increase globally. Mathematical models canhelp in developing better strategies to contain a pandemic. Considering mul-tiple measures taken by African governments and challenging socio-economicfactors, simple models cannot fit the data. We studied the dynamical evolu-tion of COVID-19 in selected African countries. We derived a time-dependentreproduction number for each country studied to offer further insights into thespread of COVID-19 in Africa.
Keywords:
COVID-19, SIDARTHE, Africa, Benin, Mozambique, Rwanda,Togo, Zambia ∗ Corresponding Authors
Email addresses: [email protected] (K´et´evi A. Assamagan), [email protected] (Somi´ealo Azote)
Preprint submitted to Scientific African July 31, 2020 a r X i v : . [ q - b i o . P E ] J u l . Introduction COVID-19 has spread to the entire world within a few months [1]. The WorldHealth Organization (WHO) predicts that 29 to 44 million Africans could beinfected with SARS-CoV-2 during the first year of the pandemic and 83 to 190thousand Africans could die if they don’t uphold containment measures [2, 3].This grim prediction suggests that most African countries have a lower trans-mission rate than the other regions of the world such as Europe, the UnitedStates of America, and China [2]. However, the low transmission rate may pro-long the outbreak over several years, putting pressure on economic resources.Most African countries are struggling because of lack of essential medical re-sources such as test kits, personal protective equipments and ventilators. Thecontainment measures such as frequent hand washing, isolation, contact trac-ing, and social distance are a challenge in Africa—around 60% of the Africanpopulation lives below the poverty line [4] and cannot afford the basic hygienicamenities. The densely populated slums of Africa make social distancing impos-sible and burdens the isolation centers. In Africa, the outbreak of COVID-19has already claimed thousands of lives, rendered millions jobless, increased in-security and poverty level. A number of studies have been performed on theevolution and impact of COVID-19 in Africa, and on the African responses tothe pandemic [5–12].Models for pandemics are necessary for understanding the cause, source,spread, and planning outbreak containment [13–22]. The simplest of thesemodels is the SIR; it describes disease transmission and propagation in threecategories, namely the susceptible, infected and recovered fractions of a popula-tion [23]. An improved version of the SIR is the SEIR model which proposes fourstages: susceptible, exposed, infectious, and removed population densities [24].Simple models for COVID-19 do not offer reliable insights or predictions toinform African policymakers [23]. The models become complex when one in-cludes more socio-economic factors. One such model is the SIDARTHE [25]which considers eight stages of epidemic evolution.2n this paper, we analyzed COVID-19 data from Benin, Mozambique, Rwanda,Togo and Zambia. We tested the SIDARTHE model on these data and estimatedbasic reproduction numbers. This may improve our understanding of the spreadof COVID-19 in Africa, although the numbers of tests are small relative to thesizes of the populations. We offer suggestions to keep the basic reproductionnumber below one, to slow and contain the spread. In Section 2, we presentthe mathematical model used in the studies reported in this paper. In Sec-tion 3, we discuss the analysis strategy and results. In Section 4, we discuss theimplications of the results, and we offer concluding remarks in Section 5
2. Model
To have confidence in a model, one needs suitable fits to existing data andverifiable predictions. Here, we describe the SIDARTHE dynamical model, de-veloped to study the spread of COVID-19 in Italy [25]. The strength of thismodel comes from the fact that it considers the various measures taken byItalian government to contain the disease [25]. It is a mean-field epidemio-logical model with eight time-dependent compartments, namely ”Susceptible”,”Infected”, ”Diagnosed”, ”Ailing”, ”Recognized”, ”Threatened”, ”Healed” and”Extinct”, as shown in Figure 1. This model describes the dynamic spread ofthe disease when social distancing, lockdown, testing, contact tracing, treat-ment, curfew, and/or quarantine are implemented as containment strategies ina population.The following mathematical system of eight differential equations describes3 ealed (H) Extinct (E)Threatened (T)Infected (I) Diagnosed (D)
Recognized (R)Susceptible (S) Ailing (A)
Contagion
Healing Death
CriticalCritical
Symptoms SymptomsDiagnosis Diagnosis 𝛼, 𝛽 , γ , δ ε λ ζ τρ ξ ν θσκ ημ SIDARTHE Parameters: ▪ 𝛂, γ : Transmission rate due to contact with UNDETECTED asymptomatic, symptomatic infected, respectively. ▪ 𝛃 , δ : Transmission rate due to contacts with DETECTED asymptomatic, symptomatic infected, respectively. ▪ 𝛆 : Detection rate for ASYMPTOMATIC ▪ 𝛉 : Detection rate for SYMPTOMATIC ▪ 𝛇 : Worsening rate, UNDETECTED asymptomatic infected becomes symptomatic ▪ 𝛈: Worsening rate , DETECTED asymptomatic infected becomes
Symptomatic ▪ 𝛍: Worsening rate, UNDETECTED symptomatic infected developlife−threatening symptoms. ▪ 𝛎 : Worsening rate, DETECTED symptomatic infected develop life -threatening symptoms. ▪ 𝛕 : Mortality rate for infected with life-threatening symptoms ▪ 𝛋, 𝛌 : Recovery rate for undetected asymptomatic, symptomatic infected, respectively. ▪ 𝛏, 𝛒: Recovery rate for detected asymptomatic,symptomatic infected, respectively.
Figure 1:
Graphical representation of different compartments of the SIDARTHE model [25]:S stands for susceptible, the total population of the case study region or country; I, infected(asymptomatic infected undetected); D, diagnosed (asymptomatic infected detected); A, ailing(symptomatic infected undetected); R, recognized (symptomatic infected, detected); T, threat-ened (infected with life-threatening symptoms, detected); H, healed (recovered); E, extinct(dead). the SIDARTHE model [25]: d S ( t )d t = − S ( t ) ( αI ( t ) + βD ( t ) + γA ( t ) + δR ( t )) , d I ( t )d t = S ( t ) ( αI ( t ) + βD ( t ) + γA ( t ) + δR ( t )) − ( λ + ε + ζ ) I ( t ) , d D ( t )d t = εI ( t ) − ( η + ρ ) D ( t ) , d A ( t )d t = ζI ( t ) − ( θ + µ + κ ) A ( t ) , d R ( t )d t = ηD ( t ) + θA ( t ) − ( ν + ξ ) R ( t ) , d T ( t )d t = µA ( t ) + νR ( t ) − ( σ + τ ) T ( t ) , d H ( t )d t = λI ( t ) + ρD ( t ) + κA ( t ) + ξR ( t ) + σT ( t ) , d E ( t )d t = τ T ( t ) . (1)The basic reproduction number, R , is an epidemiological parameter to de-scribe the contagiousness or transmissibility of infections [25]. Biological, socio-economic, environmental and behavioral factors affect R . It is a parameter used4o study the dynamics of an infectious disease. An outbreak ends if R < R > R indicates of the potential magnitude of an outbreak,and can be used to estimate the fraction of the population to be vaccinated tostop the spread. However, because of its complex dependence on many factors, R is often modeled and, as a result, depends on model parameters and as-sumptions. Therefore, one must apply R with great caution. The SIDARTHEmodel defines R as follows [25]: R = αr + β × (cid:15)r × r + γ × ζr × r + δ × η × (cid:15)r × r × r + δ × ζ × θr × r × r , (2)with r = (cid:15) + ζ + λ,r = η + ρ,r = θ + µ + κ, (3) r = ν + ξ,r = σ + τ. We adapted the SIDARTHE model to consider the containment measures takenby African countries and the impact of socio-economic conditions in Africa. InSection 3, we discuss the analyses of data from Benin, Mozambique, Rwanda,Togo and Zambia, and the application of the SIDARTHE model to these data.
3. Analysis
We collected the first three months of the official data on COVID-19 from Benin,Mozambique, Rwanda, Togo and Zambia. We got the data from the officialwebsite of each country. One team member who is a resident (or is a native) ofa country was in charge to compile and follow the measures taken. The sameteam member was also responsible to understand the tests performed in thatcountry. The data came in categories of active, recovered, dead and total cases.Compared to the SIDARTHE stages of pandemic evolution, it is straightforwardto establish the following associations: the recovered cases correspond to the5Healed” and the dead cases to ”Extinct” shown in Figure 1. The active casesdo not have a direct correspondence in the model. One needs to understandthe tests to define an association of the active cases to the model. From theeight stages in the SIDARTHE, the active cases in the data should, at the bareminimum, map to the sum of the ”Recognized and Threatened”. However,depending on whether asymptomatic or ailing persons were tested and counted,the active cases might contain some of them. To compare data to the model,we defined the active cases as the sum of the ”Recognized, ”Threatened” and”Ailing” (or ”Diagnosed”)—this is not an exact correspondence because of thecomplexity of the testing and counting procedures. In addition, the total casesalso do not map directly to any stage of the model. In the data, the total casesare the sum of the active, recovered and dead cases. In the model, we builtthe total cases as the sum of the model active cases and healed and extinctcompartments shown in Figure 1.After we defined the mapping of the data onto the model compartments orstages, we matched the model to the data by adjusting the model parametersdepending on whether the active, recovered and/or dead cases were increasingor decreasing. We solved the eight differential equations in Eq. (1) by Eulerdiscretization to estimate the parameters from best match between model anddata. Subsequently, we computed R according to Eq. (2). The result is an ex-traction of a time-dependent R from the estimated parameters. In the followingsubsections, we will discuss each country, one-by-one. They identified the first case on March 16, 2020, and the government took imme-diate containment measures such as limitation in border crossings, compulsoryquarantine of people entering the country by air, suspension of government andbusiness missions outside the country, suspension of all demonstrations and non-essential sporting, cultural, religious or political events, closure of mosques andchurches, social distance, hygiene and wearing of masks requirements. FromMarch 30 to May 11, 2020, schools and universities were closed. They imposed6 total lockdown on the regions—Cotonou, Abomey-Calavi, Allada, Ouidah,Sm-Podji, Porto-Novo, Akpro-Missrt and Adjarra—most exposed to the pan-demic. The government engaged in an awareness campaign through the mediaand the police force. They encouraged people to inform the authorities aboutanyone who returned to the country and did not self-isolate. From May 11,the government lifted the lockdown of the aforementioned regions and by June2, and activities resumed with mandatory social distance and the wearing ofmasks. We collected the official data compiled by the government and modeledit as shown in Figure 2 where one sees that there is a period between Day 54and Day 61 where they posted no data. In the top panel of Figure 2, there isa systematic shift in the data before Day 54 compared to after Day 61. Thisis because of the difference in the reporting of the test results. Before May 19,the government reported results of both the rapid diagnostic and polymerasechain reaction (PCR) tests. After May 19, following the WHO guidelines, thegovernment started reporting only the PCR test results, although they contin-ued to perform the rapid diagnostic tests. We see a good match between theSIDARTHE model and the data. The bottom panel of Figure 2 shows the result-ing time-dependent R from the modeling. We find that the basic reproductionnumber rarely exceed two; however, it fluctuates. After May 19, R rarely ex-ceeds one because only the PCR test results were being reported; however, itmay also be because of the effectiveness of the measures implemented by thegovernment. In Mozambique, they detected the first case on March 22, 2020. The individualwas a Mozambican national who had traveled to the United Kingdom. Thepatient showed mild symptoms. The health authorities placed him in isolationat home and under clinical supervision. The government closed schools anduniversities on March 23, suspended the issuance of entry visas, and cancelledthe ones already issued. They also suspended social events with over 50 people.They required travelers to self-quarantine. The country went into a state of7 igure 2:
In the top plot, we show the official data compiled by the government of Benin.Day 0 is March 16, 2020. The uncertainties shown on the data points are statistical only.We normalized the data to a population of 11.5 million. Superimposed is the SIDARTHEmodel applied to the data. The bottom plot shows the resulting R for Benin as a function oftime. emergency on April 1. They extended the state of emergency successively:on April 29 until May 30; then until the end of June; on June 28 until July29. On May 12, they suspended international flights until May 30, except forhumanitarian, cargo or state flights. However, they did not impose a lockdown.At the time of writing this article, the government and local authorities werestudying schools re-opening strategies. Figure 3 shows the COVID-19 data8 igure 3: In the top plot, we show the official data compiled by the government of Mozambique,normalized to a population of 29 million. The uncertainties shown on the data points arestatistical. Superimposed is the SIDARTHE model applied to the data. Day 0 is March 22,2020. The bottom plot shows the resulting R for Mozambique as a function of time. of Mozambique with the modeling of the SIDARTHE; in the top panel, wesee good agreement between the model and the data for all the cases of thedead, recovered and active fractions of the population. As a result, the totalcumulative cases are also well modeled. In the bottom panel of Figure 3, weshow the extracted R which remains below two for the entire period shown.The R for Mozambique fluctuates. Between Day 40 and Day 45, it droppedsignificantly. After Day 45, it stays slightly above one.9 .3. Case of Rwanda On March 14, 2020, Rwanda confirmed its first case of COVID-19. It was aforeign national who arrived in the country on March 8. The individual showedno symptoms upon arrival; however, he reported to a health facility on March 13and tested positive. They started testing symptomatic cases right away, beforethey identified the first case. Contact tracing and testing of asymptomatic casesstarted on March 14. From March 15, they postponed schools, religious activ-ities, weddings until further notice and implemented social distance measures.Because of an increase in the number of cases, the authorities took additionalsafety measures on March 21: they imposed a lockdown by closing of bars,boarders, airports and markets, except for those selling food and hygienic es-sentials. They required masks in all public places and provided markets andshops with sanitizers. Figure 4 shows the Rwanda COVID-19 data on the toppanel; we superimpose the modeling of the data and see good agreement in thedead, recovered and active cases. As a result, the total cases are also well mod-eled. From the modeling, we derived R for Rwanda as shown in the bottompanel of Figure 4. The initial R is above three, but drops well below one afterabout a week because of the swift reaction of the government and the public.After a few weeks, the R rose above one, most likely because of the difficultiesto observe the measures imposed. We see another reduction in R around Day47; around Day 64, it went up to about 1.5. Togo recorded its first case of COVID-19 on March 6, 2020; the individual wasa Togolese national who had traveled abroad. The government implementedcontainment measures right away, such as contact tracing, monitoring of per-sons under quarantine, testing of symptomatic cases, and surveillance at pointsof entry, borders and airports. After an extraordinary meeting of the councilof ministers on March 16, the government established the following measures:suspension flights from Italy, France, Germany, and Spain; cancellation of all10 igure 4:
In the top plot, we show the official data compiled by the government of Rwanda,normalized to a population of 12 million. The uncertainties shown are statistical. Day 0 isMarch 14, 2020. Superimposed on the data is the SIDARTHE model applied to the data. Thebottom plot shows the resulting R for Rwanda as a function of time. international events for three weeks; self-isolation of people coming from high-risk countries; border closure; and prohibition of events with over 100 peopleeffective from March 19. For at least two-and-a-half months, schools, univer-sities, churches, saloons, bars, etc., were closed. They imposed a curfew from9:00pm to 6:00am. They tested truck drivers crossing the borders; then theyallowed the trucks to proceed to their destinations under surveillance. If thedrivers had been in contact with confirmed cases, they placed them under quar-11 igure 5: In the top plot left, we show the official data compiled by the government ofTogo. The uncertainties shown are statistical. Day 0 is March 6, 2020. Superimposedis the SIDARTHE model applied to the data. The top right plot shows the details of theSIDARTHE model for Togo with the time evolution of the eight stages of the pandemic. Theembedded picture in the top right panel shows the distribution of the susceptible population.We normalized the top plots to a population of 8 million. The bottom left plot shows the R for Togo as a function of time. The bottom right plot shows the number of active casessuperimposed onto the number of the daily tests done in Togo. antine. On April 7, the government started massive tests of both symptomaticand asymptomatic persons in cities with over ten cases. From June 9, they liftedthe curfew. However, the government made the wearing of masks compulsory;also, they required hand washing before access to public or private services or12arkets.We used the containment measures to tune the model parameters as a func-tion of time. Figure 5 shows the data and the model; on the top left panel,we see good agreement in the dead and recovered cases. For the active cases,the agreement is good in the earlier and later time periods. The mis-modelingobserved in the middle time period is likely related to the difficulty in defin-ing accurately the active cases in the model as mentioned in Section 3. Forthe total cases, the model agrees with the data in the entire period shown. Inthe top right panel, we show the time evolutions of all the eight stages of theSIDARTHE model for Togo. The bottom left panel of Figure 5 shows the R for Togo. We see that in first 2 weeks, R was about three. It dropped in thesubsequent few weeks because of the effectiveness of the containment measuresand the social awareness campaign. However, after Day 40, the R rose; this isbecause between May 5–20, the number of cases sharply increased when neigh-boring countries re-opened their borders. This led to an influx of imported casesfrom Togolese nationals that returned to Togo. The bottom right plot showsthe number of daily tests and the active cases—the same active cases shownon the top left plot. The active cases show structures the distribution where,periodically, the cases increased or dropped. To model the data accurately, wetried to understand whether these structures were correlated with the number ofdaily tests or related to the dynamical evolution of the pandemic. As shown inthe bottom right plot of Figure 5, we found no corrections between the numberof daily tests and the active cases. Until Day 30, Togo reported only the totalnumber of tests done, not the daily test numbers. In the bottom right panel ofFigure 5, we see a flat distribution up to Day 30we took an average by dividingthe total number tests over the number of days. After Day 30, the histogramin the bottom right plot of Figure 5 shows the reported daily test numbers. On March 18, 2020, Zambia reported its first two cases of COVID-19. Zambiahosts the Southern Africa Regional Collaborating Center of the Africa CDC13Center for Disease Control) and has been coordinating the response at the re-gional level. The government has put in place a contingency plan that outlinesthe country’s preparedness. The government continues to enforce the measuresand interventions to control the spread countrywide. The public health safetymeasures implemented include the closure of schools and higher learning insti-tutions; wearing of a mask while out in public; continued screening of travelersinto Zambia; redirection of all international flights to land and depart fromKenneth Kaunda International Airport only; suspension of non-essential travelsto countries with confirmed COVID-19 cases; restriction of public gatherings;restaurants to operate only on take away and delivery basis; and closure of allbars, nightclubs, cinemas, gyms and casinos. On May 8, the control measureswere further reviewed: restaurants may revert to their normal operation; cine-mas, gyms and casinos may also reopen; they made an appeal to proprietors ofhotels, lodges, tour operators, event management companies and others—whovoluntarily closed their business to ensure the safety of their staff and clientele—to consider reopening; bars and taverns remained closed pending further reviewof the measures, depending on the evolution of the pandemic; they allowedonly examination classes in primary and secondary schools to reopen. The firstclasses reopened on June 1 with enforced public health guidelines in place: thereopening of business premises and schools is subject to adherence to publichealth regulations, guidelines and certifications. The government continues toupdate response activities on a regular basis.Figure 6 shows the COVID-19 data of Zambia and its SIDARTHE modeling.The death rate and the total cases are well modeled. The trends of the recoveredand active cases are fairly well modeled. The R for Zambia started close tothree but dropped below one within a few weeks. It rose again, and around Day50 it rose to about eight until Day 55. This is because of a significant increase inthe reported numbers of daily cases around Day 50. On May 8, the governmentdispatched a team of health workers to Nakonde—a town next to Tanzania—toprovide technical support and enhance port health services, community surveil-lance and disinfection of public places. They tested truck drivers, community14 igure 6: In the top plot, we show the official data compiled by the government of Zambia,normalized to a population of 17.5 million. We show only statistical uncertainties. Day 0is March 18, 2020. Superimposed is the SIDARTHE model applied to the data. The bottomplot shows the resulting R for Zambia as a function of time. members, health care workers, staff of lodges and the Immigration Department.The prior number of total cases was 167 and on May 9, they had 85 cases, al-most a 50% increase. Seventy-six of the 85 cases were from Nakonde. BetweenMay 9 and 16, they reported high daily cases of 174 and 208. One hundredtwenty-six of the 174 cases were from Nakonde and 196 of the 208 cases werealso from Nakonde. These increases in the daily cases, concentrated aroundNakonde, explain the high R in Day 50-55. The R dropped again around Day155 until about Day 70 when it increased above one.
4. Discussion
For the all the countries studied, R started above one with a few importedcases. Within a few weeks, R dropped below one because of the swift anddecisive reactions of the governments and the awareness campaigns. The peoplereacted well initially and followed the authorities’ directives. Unfortunately, R did not stay below one for a long period; in all the cases studied, the basicreproduction number rose again above one after a few weeks—because of diffi-culties in adhering to the measures when the people face other socio-economicchallenges. The rise of R after it had fallen initially may also because ofcomplacency, fake news, and misinformation—as mentioned in Section 1, somebelieve that COVID-19 is a scam, Africans are immune, and/or the diseasehas no impact in tropical climates. That the initial responses were effective tobring R below one is an encouragement that African countries can contain thespread. The challenge is to maintain the containment measures long enoughto bring R permanently below one. A continuous campaign of community en-gagement with regular briefings is important; so are an active combat againstfake news and misinformation. They should maintain the lockdown and socialdistance measures notwithstanding the socio-economic adversities. Economicrelief is necessary for the people with hardships exacerbated by these measures;this will motivate adherence to the containment plans and that will stop thepandemic [26–29].A comment on the studies reported in this paper is their validity, given thenumbers of limited tests performed. We show in Figure 5 that the number ofcases are not correlated with the limited number of tests. The statistical samplesused are significant; therefore, the conclusions are valid. One may extrapolatethese results to the larger populations of the countries studied to determine, forexample, the number of people to vaccinate. However, from the limited tests,we cannot extrapolate to infer the total number of infections in the country. We16lso caution extrapolating to the future to make predictions; this is because, aswe have shown in Figures 2–6, the basic reproduction number fluctuates. Onlydetailed modeling from first principles in biology, medicine, physics, epidemiol-ogy and sociology may offer a framework for viable predictions.
5. Conclusion
We have studied COVID-19 data from Benin, Mozambique, Rwanda, Togo andZambia. We modeled the data from these countries with the SIDARTHE, andextracted a time-dependent basic reproduction number for each country stud-ied. Our studies show that the initial reactions of African governments andpopulations were effective to bring the basic reproduction number below one.However, relaxation and difficulties to maintain the measures over time drivethe basic reproduction number in a time-dependent cyclic pattern of rises andfalls. We suggest that African countries find satisfactory economic supports fortheir most disadvantaged populations. This will encourage adherence to thecontainment plans.
Acknowledgements
We thank Professor John Ellis (University of London) for useful discussions.Toivo S. Mabote would like to thank Professor Doutor Cl´audio Mois´es Paulo(Universidade Eduardo Mondlane) for academic advice and mentorship. Weacknowledge support and mentorship from ASP—the African School of Fun-damental Physics and Applications. We received no financial support for thiswork.
References [1] Report of the World Health Organization-China joint mission on coron-avirus disease 2019 (COVID-19), . who . int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report . pdf (2020). 172] New World Health Organization estimates: up to 190000 peo-ple could die of COVID-19 in Africa if not controlled, . afro . who . int/news/new-who-estimates-190-000-people-could-die-covid-19-africa-if-not-controlled (2020).[3] COVID-19 Situation update for the World Health OrganizationAfrican Region, External Situation Report 17 (24 June 2020), https://reliefweb . int/report/south-africa/covid-19-situation-update-who-african-region-external-situation-report-17-24 (2020).[4] The World Bank Group, Poverty, . worldbank . org/en/topic/poverty (2020).[5] M. Massinga Loemb´e, A. Tshangela, S. J. Salyer, J. K. Varma, A. E. Og-well Ouma, J. N. Nkengasong, COVID-19 in Africa: the spread and re-sponse, Nat Med 26 (2020) 999–1003. doi:10 . .[6] M. Martinez-Alvarez, A. Jarde, E. Usuf, H. Brotherton, M. Bittaye, A. L.Samateh, A. Roca, COVID-19 pandemic in West Africa, The Lancet GlobalHealth 8(5) (2020) e631–e632. doi:10 . .[7] S. H. Ebrahim, Q. A. Ahmed, E. Gozzer, P. Schlagenhauf, Z. A. Memish,COVID-19 and community mitigation strategies in a pandemic, BMJ 368. doi:10 . . m1066 .[8] S. N. Etkind, A. E. Bone, N. Lovell, R. L. Cripps, R. Harding, I. J. Hig-ginson, K. E. Sleeman, The role and response of palliative care and hospiceservices in epidemics and pandemics: a rapid review to inform practice dur-ing the COVID-19 pandemic, Journal of Pain and Symptom Managemen60 (2020) e31–e40. doi:10 . . jpainsymman . . . .[9] O. A. Adegboye, O. O. Adesiyun, M. A. N. Adeboye, Double Sides ofCOVID-19 Pandemic: African Countries should Break Grounds or be Per-18anently Broken, West J Med & Biomed Sci 1(1) (2020) 110–114.URL https://wjmbs . com . ng/index . php/wjmbs/article/view/15/17 [10] MO Ibrahim Foundation, COVID-19 in Africa: A call for coor-dinated governance, improved health structures and better data, https://mo . ibrahim . foundation/news/2020/covid-19-africa-a-call-coordinated-governance-improved-health-structures-and-better-data (2020).[11] African Union, Impact of the Coronavirus COVID-19 on the African Econ-omy, . tralac . org/documents/resources/covid-19/3218-impact-of-the-coronavirus-covid-19-on-the-african-economy-african-union-report-april-2020/file . html (2020).[12] S. A. Lone, A. Ahmad, COVID-19 pandemic–An African perspective,Emerging Microbes and Infections 9 (2020) 1300–1308. doi:10 . . . .[13] K. van Zandvoort, C. I. Jarvis, C. Pearson, N. G. Davies, T. W. Russell, A.J. Kucharski, M. J. Jit, S. Flasche, R. M. Eggo, F. Checchi, and CMMIDCOVID-19 working group, Response strategies for covid-19 epidemics inafrican settings: a mathematical modeling study, medRxiv . doi:10 . . . . .[14] Z. Zhao, X. Li, G. Liu, F.and Zhu, C. Ma, L. Wang, African countries andimplications for prevention and controls: A case study in South Africa,Egypt, Algeria, Nigeria, Senegal and Kenya, Science of the Total Environ-ment (2020) 138959. doi:10 . . scitotenv . . .[15] D. B. Abila, A. Yusuff Adebayo, D. E. Ekpenyong, A.and Lucero-Prisno III,S. Aggrey, M. Britha, P. Bugingo, The First Eighty-Four (84) Days ofCOVID-19 in Africa: Analysis of Incidence and Deaths Associated withCOVID-19, SSRN (2020) 1–23. doi:10 . . .1916] H. Bendaif, B. Hammouti, I. Stiane, Y. Bendaif, M. A. El Ouadi,Y. El Ouadi, Investigation of spread of novel coronavirus (COVID-19) pan-demic in MOROCCO and estimated confinement duration to overcome thedanger phase, Caspian Journal of Environmental Sciences 18(2) (2020) 149–156. doi:10 . . . .URL https://cjes . guilan . ac . ir/article 4070 . html [17] A. Medinilla, B. Byiers, P. Apiko, African regional responses to COVID-19, https://ecdpm . org/publications/african-regional-responses-covid-19/ (2020).[18] M. Dahab, K. van Zandvoort, S. Flasche, A. Warsame, P. B. Spiegel,R. J. Waldman, F. Checchi, COVID-19 control in low-income set-tings and displaced populations: what can realistically be done?, . lshtm . ac . uk/newsevents/news/2020/covid-19-control-low-income-settings-and-displaced-populations-what-can (2020).[19] E. S. McBryde, M. T. Meehan, O. A. Adegboye, A. I. Adekunle, J. M. Cald-well, A. Pak, J. M. Trauer, Role of modeling in COVID-19 policy develop-ment, Pediatric Respiratory Reviews . doi:10 . . prrv . . . .[20] S. A. Lauer, K. H. Grantz, Q. Bi, F. K. Jones, Q. Zheng, H. R. Meredith,J. Lessler, The incubation period of coronavirus disease 2019 (COVID-19)from publicly reported confirmed cases: estimation and application, Annalsof Internal Medicine 172(9) (2020) 577–582. doi:10 . .[21] M. T. Meehan, D. P. Rojas, A. I. Adekunle, O. A. Adegboye, J. M. Cald-well, E. Turek, E. S. McBryde, Modeling insights into the COVID-19 pan-demic, Pediatric Respiratory Reviews . doi:10 . . prrv . . . .[22] J. K. K. Asamoah, M. A. Owusu, Z. Jin, F. T. Oduro, A. Abidemi, E. O.Gyasi, Global stability and cost-effectiveness analysis of COVID-19 con-sidering the impact of the environment: using data from Ghana, Chaos,Solitons and Fractals 110103. doi:10 . . chaos . . .2023] M. Sinkala, P. Nkhoma, M. Zulu, D. Kafita, R. Tembo, V. Daka, Thecovid-19 pandemic in africa: Predictions using the sir model indicate thecases are falling, medRxiv (2020) 2020.06.01.20118893. doi:10 . . . . .[24] J. B. Nachega, A. Grimwood, H. Mahomed, G. Fatti, W. Preiser,O. Kallay, D. Ngamije, From easing lockdowns to scaling-up community-based COVID-19 screening, testing, and contact tracing in Africa: sharedapproaches, innovations, and challenges to minimize morbidity and mor-tality, Clinical Infectious Diseases ciaa695. doi:10 . .[25] G. Giordano, F. Blanchini, R. Bruno, P. Colaneri, A. Di Filippo, A. Di Mat-teo, M. Colaneri, Modeling the COVID-19 epidemic and implementation ofpopulation-wide interventions in Italy, Nature Medicine 26 (2020) 855–860. arXiv:2003 . . PE] , doi:10 . .[26] A. Otu, B. Ebenso, R. Labonte, S. Yaya, Tackling COVID-19: Can theAfrican continent play the long game?, Journal of Global Health 10(1). doi:10 . . . .[27] S. Mehtar, W. Preiser, N. A. Lakhe, A. Bousso, J.-J. M. TamFum,O. Kallay, M. Seydi, A. Zumla, J. B. Nachega, Limiting the spread ofCOVID-19 in Africa: one size mitigation strategies do not fit all countries,The Lancet Global Health 8(7) (2020) e881e883. doi:10 . .[28] C. Ihekweazu, E. Agogo, Africas response to COVID-19, BMC Medicine18 (2020) 1–3. doi:https://doi . org/10 . .[29] P. J. Rosenthal, J. G. Breman, A. A. Djimde, C. C. John, M. R. Kamya,R. G. Leke, M. R. Moeti, J. Nkengasong, D. G. Bausch, COVID-19: shin-ing the light on Africa, The American Journal of Tropical Medicine andHygiene 102(6) (1145) 1–28. doi:10 . .20-0380