Epidemiological Description of COVID-19 Cases at selected Counties in Kenya that border Uganda and Tanzania, March-July 2020

Gladys Mutethya Francis^1,2,&, Josephine Waihuini Ihahi^1,2, Adow Aden Buul^1,2, Florence Wanjiru Mugo^1,2, Robert Mburu Kuria^1,2, Kenneth Kipkoech Korir¹, Sora Jatani Biid¹, Julius Shem Otwabe¹, Ahmed Abade Mohamed¹, Waqo Boru¹, Elvis Omondi Oyugi¹, Maurice Omondi Owiny^1,3, Josephine Githaiga¹, Fredrick Odhiambo¹

 

¹Field Epidemiology and Laboratory Training Program, Ministry of Health, Kenya, ²School of Public Health, Moi University, Kenya, ³African Field Epidemiology Network (AFENET)

 

 

^&Corresponding author
Gladys Mutethya Francis, Field Epidemiology and Laboratory Training Program, Ministry of Health, Kenya

 

 

 

 

The first COVID-19 case in Kenya, a resident of Kajiado County who had traveled back to Nairobi from the United States of America (USA) via London, United Kingdom on March 5, 2020, was confirmed on March 12, 2020 [1]. Globally, as of 12 July 2020, there were 12,552,765 COVID-19 cases with 561,617 deaths (Case Fatality Rate; CFR=4.5%), with Africa recording 461,296 (3.7%) cases and 8,092 (1.4%) deaths (CFR=1.8%) [2]. During this time Kenya had reported a total of 10,105 cases. Of these, 1,061 (11%) were symptomatic and 185 deaths (CFR= 1.8%) [3]. The subsequent COVID-19 cases were mostly detected in the counties that border Tanzania and Uganda. Generally, international points of entry are at an increased risk of recording a higher number of COVID-19 cases, especially when they host the ground crossing points allowing movement of goods through the transport corridor as regulated by the International Health Regulations (IHR) 2005 [4].

 

Long-distance truckers, who include truck drivers and loaders are mobile and traverse several countries putting them at high risk of being infected with COVID-19, consequently being possible super transmitters of the infection as they interact with diverse populations while on transit along the transport corridors [5]. Therefore, truckers can import COVID-19 infection from either of the seven countries served by the northern and central transport corridors, spanning from the Democratic Republic of Congo through Burundi, Rwanda, South Sudan, Tanzania, Uganda to Kenya; from Mombasa to Busia-Malaba through Nakuru County [6]. Three counties in Kenya border Uganda namely: Busia, Bungoma, and Trans Nzoia. On the other hand, four counties in Kenya border Tanzania namely: Kajiado, Migori, Taita Taveta, and Kwale. Of 10,105 cases reported in Kenya, 1,301 (12.9%) were from the border counties with Busia having 550 (5.4%), Kajiado having 458 (4.5%), Migori having 190 (1.9%), Kwale having 54 (0.5%), Bungoma having 8 (0.1%), and Taita Taveta having 41 (0.4%) cases.

 

Initially, the strategy for confirmation of COVID-19 cases in Kenya involved the testing of all identified contacts to a confirmed case regardless of whether they had symptoms or not. This had been changed to targeted testing in April 2020 due to global supply chain challenges in laboratory testing kits, reagents, and supplies [7]. The target populations per the Ministry of Health Guidelines included all frontline health workers, truck drivers, food handlers, institution workers, airline crew, port health and non-health staff. Therefore, the testing of truckers at crossing border points of Kenya, Uganda, and Tanzania was introduced to curb the spread of the COVID-19 virus between these countries. It strengthened surveillance while simultaneously facilitating the movement of goods and services between member states amid the pandemic. Thus, compared to other areas in border counties, more cases were detected and reported through cross-border surveillance initiatives in host sub-counties and within a short time, an exponential increase of cases was reported. This resulted in the establishment of guidelines on May 12, 2020, that restricted the free movement of people by the East African Community [8]. Therefore, it became mandatory for the truckers to be tested before entry/exit into the country, at a time when community transmission was indefinitely widespread [8]. To further enhance COVID-19 surveillance and to reduce Turn Around Time (TAT) for the test results, Kenya prioritized COVID-19 testing for the truckers by establishing two testing mobile laboratories at Namanga border in Kajiado county and Malaba border in Busia county. Despite the measures, COVID-19 infection continued to escalate in the border counties necessitating epidemiological description of the cases to guide the control measures. This study will inform future public health actions leading to the strengthening of the COVID-19 response especially at the borders of an African country.

 

 

Study Sites and design

 

Kenya is one of the countries in East Africa. It borders Tanzania to the south, Somalia to the east, Ethiopia and Sudan to the north, and the Indian Ocean to the southeast. Kenya is divided into forty-seven (47) counties and three hundred and seven (307) sub-counties. We reviewed COVID-19 data for the period March 13, 2020, through July 12, 2020, for the four border counties of Busia, Migori, Kajiado, and Taita Taveta. These counties were purposively selected due to the escalating number of cases reported in those areas and to assess COVID-19 surveillance in borders serving the East Africa community. The East Africa community is composed of six partner states namely Kenya, Tanzania, Uganda, Burundi, Rwanda, and South Sudan Figure 1.

 

Study population and data collection

 

We reviewed records of the confirmed COVID-19 cases in Busia, Migori, Kajiado, and Taita Taveta counties. A positive Real-Time Polymerase Chain Reaction (RT-PCR) test on nasopharyngeal and/or oropharyngeal swab was considered a confirmed COVID-19 case per the Ministry of Health (MOH) protocol [7]. We reviewed health records of outpatient registers (MOH 204 A & MOH 204 B), inpatient registers (patient´s files), and laboratory registers (MOH 240) from March 13, 2020, through July 30, 2020. We abstracted demographic variables from outpatient registers, clinical and health outcome variables from patient files, and laboratory test results from laboratory registers. We compiled the data and created a line list, which included confirmed cases only.

 

Data management and Analysis

 

Data were entered and analyzed using Microsoft excel 2007. Median and Interquartile Range (IQR) were used to describe unevenly distributed continuous data. Frequencies and proportions were calculated for categorical data. We calculated the Case Fatality Rate with numerators as the number of deaths from COVID-19 and the denominator as the number of cases. The timeline for the epidemic was described using the epidemiological curve with time on the x-axis and the number of cases on the y-axis. The output from the analysis was presented using tables and figures.

 

Ethical Considerations

 

The investigation being an acute public health event, approval from the institutional review board was not sought, however, the investigation protocol was approved by the Ministry of Health. Permission to access health records was sought and granted by the Department of Health in the respective county governments. To ensure confidentiality, patients were de-identified using codes. Data were managed and stored in password-protected computers and databases.

 

 

All the line-listed 1,096 COVID-19 cases were included in the analysis. Of these 936 (86%) were male, 380 (34.7%) were in the age group of 26-35 years and 9 (0.8%) were children aged 0-5 years. The median age of the cases was 35 (IQR=15 years). Six deaths were reported (CFR=0.5%). All the deaths were male; five (83%) from Kajiado county and one (17%) from Busia county. Only 618 (56.4%) cases had their occupation variable recorded of which, 440 (71.2%) were truckers while 39 (6.3%) were health care workers Table 1.

 

Ninety-three (8.5%) of the cases were symptomatic; 42 (45.2%) had a cough, 27 (29%) had fever, and 10 (10.8%) had breathing difficulties Figure 2. A total of 26 (28%) of the cases had multiple signs and symptoms with 17 (63.4%) having both cough and fever and 9 (34.6%) having fever, cough, and breathing difficulties.

 

The majority of the cases, 493 (45%) were from Busia county, while 39 (3.6%) were from Taita Taveta county. Border sub-counties in the study counties disproportionately reported more cases compared to the rest of the sub-counties with Teso North in Busia county reporting the highest number of cases at 298 (27.2%), and Taveta sub-county in Taita Taveta county reporting the least number of cases at 21 (1.9%) Table 2.

 

The epidemic curve depicted a propagated outbreak. After the start of the mandatory testing at border crossing points on epi week 17 and targeted mass testing on epi week 18, cases detected increased steadily. There was the first peak on epi week 23, after which, a decline of cases was recorded for two consecutive weeks of 24 and 25. The second peak was reported on epi week 26 and cases started to decline again towards the end of the month Figure 3.

 

 

Our study findings indicated that initiation of mandatory testing in border points, as well as the start of targeted mass testing, bolstered COVID-19 case detection rates in border areas from May 2020 [9]. This followed the WHO's advice to countries on the importance of testing during the early stages of the pandemic [10]. In addition to the WHO advisory and realizing that there was a possibility of local transmission in Kenya, strategies such as targeted testing to break transmission at the community level were initiated by the Ministry of Health [7]. There was a restriction on the movement of passengers across some points of entry, but the movement of essential goods and services in and out of the East African corridors during the pandemic was exempted [8]. These measures including the mandatory testing of truckers in all border points may have contributed to the prevention of importation of cases into neighboring countries.

 

The propagated epidemic curve showed that COVID-19 infection was spread through a person-to-person interaction. Literature has shown that respiratory droplets of an infected individual spread to a nearby person directly through speaking, coughing, or sneezing or indirectly when a person touches surfaces or objects contaminated with the virus [11-13]. To prevent such, WHO recommended persons to maintain a physical distance of at least 1 meter or more [14]. This finding was in unanimity with the preliminary study on the COVID-19 outbreak done in China that showed the existence of human-to-human transmission although further studies to support the findings were recommended [15].

 

Persons of all ages were susceptible to COVID-19 infection. However, the study showed higher infection in adults aged between 26-45 years as compared to children aged below the age of five years. This finding could be attributed to age-specific roles, where adults tend to interact with more diverse people as they engage in their daily activities as compared to children whose interactions are mostly limited to their caretakers or playmates at home. A study conducted in China found young adults engaged more in outdoor productive activities making a livelihood [16]. A similar situation was observed among the young adults in border towns who are either employed as truck crew or run businesses ranging from lodging services to retail trading. These activities could lead to interactions that may increase the risk of acquiring COVID -19 in these border towns. With the fear of children contracting the virus, the government of Kenya through the Ministry of Labor and Social Protection had advised parents and caregivers to limit their children's outdoor activities and interaction with people other than close family members [17]. This parental guidance and restriction could equally have contributed to low numbers of observed COVID-19 infections being reported in young children. One article suggested that although children are as susceptible to COVID-19 as adults, they exhibited milder or no symptoms compared to adults and therefore were less likely to be screened thus contributing to overall low caseloads [18]. Another study attributed reduced susceptibility to COVID -19 infection in children to non-specific immunity from frequent respiratory infection or other coronaviruses [19].

 

Morbidity and mortality of COVID-19 infections disproportionately occurred in men compared to women. Some studies have attributed such findings to risky social behaviors and cultural practices that predispose men such as alcohol consumption, smoking, reduced frequency of handwashing, and poor health-seeking behaviors [20]. Biological factors such as the difference in immunity response between men and women are also being inconclusively advanced to explain this outcome [21,22]. A study of 425 patients showed comparable findings, where 56% of the COVID-19 cases were men [23]. Another recent study done in China dispels this since they realized similar susceptibility to COVID-19 infection for both males and females, although males still exhibited worse health outcomes compared to their female counterparts independent of age [24].

 

The truckers formed the majority of the cases reported at the border counties with Busia having the majority of the cases. This may be attributed to the fact that quite some landlocked countries in East and Central Africa use Busia border point for the transport of goods and services meaning more truckers use the point compared to other border points. The higher proportion of truckers could have also been due to the implementation of mandatory testing at crossing points that required truckers to possess a valid COVID-19 negative test certificate from their country of origin before proceeding to their next destination [25]. This meant that those without valid certificates could not proceed with their journey and had to look for alternative accommodation in the border counties as they waited to be tested. Since some COVID-19 cases could be asymptomatic while being capable of transmitting the virus, there were possibilities of interactions between infected and uninfected individuals in the border counties with limited accommodation facilities. WHO housing and health guidelines 2018 report showed a strong association between crowding and airway infections [26]. Another study done by Bo Burstro¨m et al reported an increased risk of COVID-19 infection in occupations that do not permit working from home for example transportation and health care services [27]. Similar findings in a follow-up study done in Asia found that drivers and transport workers were among the top five occupations with high COVID-19 cases after health care workers [28]. In the USA, long-haul truck drivers were found to be at risk of spreading COVID-19 infection along the transportation route [29].

 

Several healthcare workers in the border counties contracted the disease, however, it was not clear whether they acquired it from the workplace or in the community. Even though mandatory testing for truck drivers was being implemented, there could have been instances where asymptomatic or mild symptomatic cases could have sought medical attention at health facilities leading to transmission to health care workers and other close contacts. According to a WHO report, frontline health care workers remained at risk of COVID-19 infection and may have continued to be infected due to several reasons with reports of about 10% of all global cases being among health workers, while 14 countries in sub-Saharan Africa accounted for more than 5% of these cases [30] Most of the cases from the study finding were asymptomatic. Although studies done during the early stages of the outbreak were not explicit on the possibility of the asymptomatic person spreading the infection, evidence of transmission of COVID-19 by asymptomatic carriers has also been shown to exist [31,32]. Having unknown asymptomatic persons in the community may pose a huge challenge to the prevention and control of COVID-19 especially where there is evidence of local transmission. Unlike in our current findings of 91.5% case-patients being asymptomatic, other studies done in Beijing, South Korea, and Nigeria however, found few COVID-19 asymptomatic cases among the confirmed patients [33-35].

 

Although mitigation measures have proved to be successful in reducing the rate of transmission of infectious diseases in many parts of the world [36], this was not the case in areas that host international points of entry where people operate through informal crossing points negating health guidelines and possibly contributing to widespread local transmission [37]. Poor coordination between countries was a major setback to proper implementation of these measures and each country was seen to independently execute the measures instead [38]. There was an increased number of cases reported in border counties and more so, the truck crew might have been because of mandatory testing initiated by the East African Community member states.

 

This study was not without limitation as the government had restricted access to health records on COVID-19, this might have led to an underestimation of our findings. However, to mitigate this the investigation team sought permission from the relevant offices. Missing variables, like information on occupation, in the data was also a limitation which was mitigated by calling cases whose contact numbers were available.

 

 

Border sub-counties that host international points of entry had a higher number of COVID-19 cases with a majority reported at the Busia border between Uganda and Kenya. Most cases were young adults and long-distance truckers. It is imperative to develop a strategy that would properly manage the movement of people at the border sub-counties to curb further spread of COVID-19 infections and institute prevention and control measures targeting the youth and young adults. The collaborative approach among countries in the containment of the disease should be enhanced especially at busy border points like Busia.

 

 

The authors declare no competing interests.

Funding

The study was funded by the Centers of Disease Control and Prevention (CDC)

 

 

Conception and design: GM, JI, AB, FM, RK, AA, KK, SJ, SO, WB, EO, MO, JG, FK, and FO. Data Collection: GM, JI, AB, FM, RK, KK, SJ, SO, EO, MO, and FO. Data Analysis: GM, JI, AB, FM, RK, AA, KK, SJ, SO, EO, MO, and FO. Development of the Manuscript: GM, JI, AB, FM, RK, AA, KK, SJ, SO, WB, EO, MO, JG, and FO. All authors read and approved the final version of the manuscript.

 

 

We wish to acknowledge the Ministry of Health Kenya and county Governments where we conducted the study for allowing us to carry out the study. The authors thank the various county and sub-county disease surveillance officers and health management teams for integrating the investigators into their activities during the study period and for allowing the team to access the data.

 

 

Table 1: Demographic characteristics of COVID - 19 cases diagnosed at Border Counties, Kenya, March - July 2020

Table 2: COVID - 19 cases diagnosed at Sub County of the border counties, Kenya, March - July 2020

Figure 1: Map of Kenya showing the Study Counties and the Neighboring Countries

Figure 2: Symptoms shown by COVID-19 cases diagnosed at Border Counties, Kenya, March-July 2020 (n=93)

Figure 3: Epicurve showing the COVID-19 outbreak at selected border counties of Kenya, March—July 2020

 

 

1. Ministry of Health Kenya. First case of coronavirus disease confirmed in Kenya. Kenya: Ministry of Health. 2020. Accessed 27 September 2020.


2. WHO. Coronavirus disease (COVID-19) Situation Report-174. WHO. 2020. Accessed 30 August 2021.


3. Ministry of Health, Kenya. Summary of COVID-19 Status_Press release -07-12-2020-17.06.05. Kenya: Ministry of Health. 2020. Accessed 30 August 2021.


4. World Health Assembly. Health regulations third edition (2005). World Health Assembly. 2005. Accessed 1 October 2020.


5. Bajunirwe F, Izudi J, Asiimwe S. Long-distance truck drivers and the increasing risk of COVID-19 spread in Uganda. Int J Infect Dis. 2020 Sep; 98:191-193.https://doi.org/10.1016/j.ijid.2020.06.085 PubMed | Google Scholar


6. Northern Corridor Transit and Transport Co-ordination Authority(NCTTCA). Northern Corridor Transit and Transport Coordination Authority: Northern Corridor Maps. Kenya: NCTTCA.


7. Ministry of Health Kenya. Targeted-Testing-Strategy-for-COVID-19-in-Kenya. Kenya: Ministry of Health. 2020. Accessed 30 August 2021. PubMed | Google Scholar


8. East African Community (EAC). EAC administrative guidelines to facilitate movement of goods services during the covid-19 pandemic signed-pages-57-22.pdf. EAC. 2020. Accessed 30 August 2021.


9. EAC. Communiqué: heads of state consultative meeting of the east african community. EAC. 2020. Accessed 28 September 2020.


10. World Health Organization (WHO). (Laboratory testing for coronavirus disease 2019 (COVID-19) in suspected human cases, interim guidance. WHO. 2020. Accessed 25 September 2020.


11. Liu J, Liao X, Qian S, Yuan J, Wang F, Liu Y, Wang Z, Wang FS, Liu L, Zhang Z. Community Transmission of Severe Acute Respiratory Syndrome Coronavirus 2, Shenzhen, China, 2020. Emerg Infect Dis. 2020 Jun; 26(6):1320-1323.https://doi.org/10.3201/eid2606.200239 PubMed | Google Scholar


12. Communicable Disease and Information Management, Communicable Diseases Branch.Guideline: Peripheral Intravenous Catheter (PIVC) [Internet]. Department of Health, Queensland Government; 2015 [cited 2022 Feb 24].


13. World Health Organization (WHO). What do we know about COVID-19 transmission?. WHO. 2020. Accessed 27 September 2020.


14. WHO. Infection prevention and control during health care when novel coronavirus ( nCoV) infection is suspected: interim guidance. WHO. 2020. Accessed 24 January 2022. Google Scholar


15. Parry J. China coronavirus: cases surge as official admits human to human transmission. BMJ. 2020; 368:m236. https://doi.org/10.1136/bmj.m236 Google Scholar


16. Liu H, Lou WQ. Patterns of productive activity engagement among older adults in urban China. Eur J Ageing. 2016 Jun 14; 13(4):361-372.https://doi.org/10.1007/s10433-016-0387-y PubMed | Google Scholar


17. Republic of Kenya Ministry of Labour and Social Protection, State Department for Social Protection, Office of the Principal Secretary. Preventing and Responding to COVID-19 Key messages to support those working with vulnerable children and families in Kenya. USAID. 2020. Accessed 23 September 2020.


18. COVID-19 National Emergency Response Center, Epidemiology and Case Management Team, Korea Centers for Disease Control and Prevention. Coronavirus Disease-19: The First 7,755 Cases in the Republic of Korea. Osong Public Health Res Perspect. 2020 Apr; 11(2):85-90.https://doi.org/10.24171/j.phrp.2020.11.2.05 PubMed | Google Scholar


19. Davies NG, Klepac P, Liu Y, Prem K, Jit M, CMMID COVID-19 working group, Pearson CAB, Quilty BJ, Kucharski AJ, Gibbs H, Clifford S, Gimma A, van Zandvoort K, Munday JD, Diamond C, Edmunds WJ, Houben RMGJ, Hellewell J, Russell TW, Abbott S, Funk S, Bosse NI, Sun YF, Flasche S, Rosello A, Jarvis CI, Eggo RM. Age-dependent effects in the transmission and control of COVID-19 epidemics. Nat Med. 2020;26(8):1205-1211.https://doi.org/10.1038/s41591-020-0962-9 Google Scholar


20. Griffith DM, Sharma G, Holliday CS, Enyia OK, Valliere M, Semlow AR, Stewart EC, Blumenthal RS. Men and COVID-19: A Biopsychosocial Approach to Understanding Sex Differences in Mortality and Recommendations for Practice and Policy Interventions. Prev Chronic Dis. 2020 Jul 16; 17:E63. https://doi.org/10.5888/pcd17.200247 PubMed | Google Scholar


21. Gebhard C, Regitz-Zagrosek V, Neuhauser HK, Morgan R, Klein SL. Impact of sex and gender on COVID-19 outcomes in Europe. Biology of Sex Differences. 2020; 11(1):1-13.https://doi.org/10.1186/s13293-020-00304-9 PubMed | Google Scholar


22. Oudit GY, Pfeffer MA. Plasma angiotensin-converting enzyme 2: novel biomarker in heart failure with implications for COVID-19. Eur Heart J. 2020 May 14; 41(19):1818-1820. https://doi.org/10.1093/eurheartj/ehaa414 PubMed | Google Scholar


23. Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, Ren R, Leung KSM, Lau EHY, Wong JY, Xing X, Xiang N, Wu Y, Li C, Chen Q, Li D, Liu T, Zhao J, Liu M, Tu W, Chen C, Jin L, Yang R, Wang Q, Zhou S, Wang R, Liu H, Luo Y, Liu Y, Shao G, Li H, Tao Z, Yang Y, Deng Z, Liu B, Ma Z, Zhang Y, Shi G, Lam TTY, Wu JT, Gao GF, Cowling BJ, Yang B, Leung GM, Feng Z. Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. N Engl J Med. 2020 Mar 26; 382(13):1199-1207.https://doi.org/10.1056/nejmoa2001316 PubMed | Google Scholar


24. Jin J-M, Bai P, He W, Wu F, Liu X-F, Han D-M, Liu S, Yang J-K. Gender Differences in Patients With COVID-19: Focus on Severity and Mortality. Front Public Heal. 2020; 8:152.https://doi.org/10.3389/fpubh.2020.00152 Google Scholar


25. Ministry of Health Kenya. Protocols-for-control-of-Covid-19-at-ground-crossing-along-Tran.pdf. Kenya: Ministry of Health. 2020. Accessed 24 January 2022.


26. World Health Organization. WHO Housing and health guidelines. WHO. 2018. Accessed 24 January 2022.


27. Burström B, Tao W. Social determinants of health and inequalities in COVID-19. Eur J Public Health. 2020 Aug 1; 30(4):617-618.https://doi.org/10.1093/eurpub/ckaa095 PubMed | Google Scholar


28. Lemke MK, Apostolopoulos Y, Sönmez S. A novel COVID-19 based truck driver syndemic? Implications for public health, safety, and vital supply chains. Am J Ind Med. 2020 Aug; 63(8):659-662.https://doi.org/10.1002/ajim.23138 PubMed | Google Scholar


29. Wells CR, Sah P, Moghadas SM, Pandey A, Shoukat A, Wang Y, Wang Z, Meyers LA, Singer BH, Galvani AP. Impact of international travel and border control measures on the global spread of the novel 2019 coronavirus outbreak. Proc Natl Acad Sci U S A. 2020; 117(13):7504-7509.https://doi.org/10.1073/pnas.2002616117 PubMed | Google Scholar


30. WHO AFRO. Over 10 000 health workers in Africa infected with COVID-19. WHO AFRO. 2020. Accessed 17 September 2020.


31. Gandhi M, Yokoe DS, Havlir D V. Asymptomatic transmission, the achilles´ heel of current strategies to control Covid-19. New England Journal of Medicine. 2020; 382(22):2158-2160.https://doi.org/10.1056/nejme2009758 PubMed | Google Scholar


32. Yu X, Yang R. COVID–19 transmission through asymptomatic carriers is a challenge to containment. Influenza Other Respi Viruses. 2020; 14(4):474-475.https://doi.org/10.1111/irv.12743 PubMed | Google Scholar


33. Tian S, Hu N, Lou J, Chen K, Kang X, Xiang Z, Chen H, Wang D, Liu N, Liu D, Chen G, Zhang Y, Li D, Li J, Lian H, Niu S, Zhang L, Zhang J. Characteristics of COVID-19 infection in Beijing. J Infect. 2020; 80(4):401-406.https://doi.org/10.1016/j.jinf.2020.02.018 PubMed | Google Scholar


34. Kim G -u, Kim M-J, Ra SH, Lee J, Bae S, Jung J, Kim S-H. Clinical characteristics of asymptomatic and symptomatic patients with mild COVID-19. Clin Microbiol Infect. 2020; 26(7):948.e1-948.e3.https://doi.org/10.1016/j.cmi.2020.04.040 PubMed | Google Scholar


35. Bowale A, Abayomi A, Idris J, Omilabu S, Abdus-Salam I, Adebayo B, Opawoye F, Finnih-Awokoya O, Zamba E, Abdur-Razzaq H, Erinoso O, Onasanya T, Ramadan P, Nyenyi S, Aniaku E, Balogun M, Okunromade O, Adejumo O, Adesola S, Ogunniyan T, Balogun M, Osibogun A. Clinical presentation, case management and outcomes for the first 32 COVID-19 patients in Nigeria. Pan Afr Med J. 2020; 35(Supp 2):24.https://doi.org/10.11604/pamj.supp.2020.35.2.23262 PubMed | Google Scholar


36. Costantino V, Heslop DJ, MacIntyre CR. The effectiveness of full and partial travel bans against COVID-19 spread in Australia for travellers from China during and after the epidemic peak in China. J Travel Med. 2020 Aug 20; 27(5):taaa081.https://doi.org/10.1093/jtm/taaa081 PubMed | Google Scholar


37. Laborde D, Martin W, Swinnen J, Vos R. COVID-19 risks to global food security. Science. 2020;369(6503):500-502. https://doi.org/10.1126/science.abc4765 Google Scholar


38. Chepkonga F. Cross-border issues in East Africa. Policy Network. 2020. Accessed 26 September 2020.