Supplement article - Research | Volume 5 (1): 8. 24 Feb 2022 | 10.11604/JIEPH.supp.2022.5.1.1151

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

Gladys Mutethya Francis, Josephine Waihuini Ihahi, Adow Aden Buul, Florence Wanjiru Mugo, Robert Mburu Kuria, Kenneth Kipkoech Korir, Sora Jatani Biid, Julius Shem Otwabe, Ahmed Abade Mohamed, Waqo Boru, Elvis Omondi Oyugi, Maurice Omondi Owiny, Josephine Githaiga, Fredrick Odhiambo

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

Received: 04 Dec 2020 - Accepted: 20 Dec 2021 - Published: 24 Feb 2022

Domain: Public health

Keywords: Kenya, COVID-19, RT-PCR, symptomatic, Point of Entry

This articles is published as part of the supplement Preparedness and response to COVID-19 in Africa (Volume 2), commissioned by AFRICAN FIELD EPIDEMIOLOGY NETWORK (AFENET).

©Gladys Mutethya Francis et al. Journal of Interventional Epidemiology and Public Health (ISSN: 2664-2824). This is an Open Access article distributed under the terms of the Creative Commons Attribution International 4.0 License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Cite this article: Gladys Mutethya Francis et al. Epidemiological Description of COVID-19 Cases at selected Counties in Kenya that border Uganda and Tanzania, March-July 2020. Journal of Interventional Epidemiology and Public Health. 2022;5(1):8. [doi: 10.11604/JIEPH.supp.2022.5.1.1151]

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

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

Gladys Mutethya Francis1,2,&, Josephine Waihuini Ihahi1,2, Adow Aden Buul1,2, Florence Wanjiru Mugo1,2, Robert Mburu Kuria1,2, Kenneth Kipkoech Korir1, Sora Jatani Biid1, Julius Shem Otwabe1, Ahmed Abade Mohamed1, Waqo Boru1, Elvis Omondi Oyugi1, Maurice Omondi Owiny1,3, 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




Introduction: World Health Organization (WHO) declared coronavirus disease of 2019 (COVID-19) a pandemic on March 11, 2020. By July 12, 2020, the cases had reached 12,552,765 globally with Kenya reporting 10,105 cases, a majority reported from counties hosting International Points of Entry (POEs). Transmission has continued across the border counties despite containment measures, thereby necessitating an epidemiological description of the cases to guide control measures.


Methods: Health records were reviewed in four counties bordering Uganda and Tanzania from March 13, 2020, through July 12, 2020, using standard COVID-19 case definitions. A positive Real-Time PCR test result on the nasopharyngeal or oropharyngeal swab was considered a confirmed case. Demographic, clinical, health outcome data were abstracted. Descriptive statistics were calculated using Microsoft Excel 2007.


Results: We reviewed 1,096 records: 941 (86%) were males, 9 (0.8%) were children aged ≤5years, the median age was 35 years (IQR=15 years). There were six deaths (CFR= 0.5%). Of the 93 (8.5%) symptomatic cases, 42 (45.2%) had a cough, 27 (29.0%) fever, 10 (10.8%) breathing difficulties and 7 (7.5%) sore throat. Nine (27.9%) cases had multiple symptoms of cough, fever, and breathing difficulties. A total of 723 (66%) were from sub-counties hosting POEs; the majority 298 (41.2%) were from Teso North in Busia County, 21 (2.9%) in Taita Taveta County, 440 (40.1%) were truckers and 39 (3.4%) were health care workers.


Conclusion: Sub-counties with POEs had more COVID-19 cases than those without POEs. Most cases were young adults and long-distance truckers. To reduce the spread of COVID-19 there is a need to institute prevention and control measures targeting young adults and truckers.



Introduction    Down

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.



Methods Up    Down

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.



Results Up    Down

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.



Discussion Up    Down

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.



Conclusion Up    Down

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.

What is known about this topic

  • COVID-19 is a global public health concern and was declared a pandemic by WHO
  • Susceptible populations need to be protected to reduce transmission
  • Truck drivers and long distance travelers were presumed to spread COVID-19 along the transport corridors

What this study adds

  • Countries should have a harmonized way of handling truck drivers to prevent spread of COVID -19 including provision of quarantine facilities, and social amenities
  • Informal crossing points should be monitored
  • There is a need to create awareness to the community on the disease and the prevention measures to take as they interact with truck drivers to prevent spread



Competing interests Up    Down

The authors declare no competing interests.


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



Authors' contributions Up    Down

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.



Acknowledgements Up    Down

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.



Tables and figures Up    Down

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



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


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


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

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