Temporal changes in the positivity rate of common enteric viruses among paediatric admissions in coastal Kenya, in the period spanning the COVID-19 pandemic, 2019-2022 (preprint)

Abstract Background The non-pharmaceutical interventions (NPIs) implemented to curb the spread of SARS_CoV_2 early in the COVID_19 pandemic years, disrupted the activity of other respiratory viruses. There is limited data from low and middle income countries (LMICs) to determine whether COVID_19 NPIs also impacted the epidemiology of enteric viruses. We investigated the changes in infection patterns of common enteric viruses among hospitalised children who presented with diarrhoea to a referral hospital in coastal Kenya, in the period spanning the COVID_19 pandemic. Methods A total of 870 stool samples from children under 13 years of age admitted to Kilifi County Hospital between January 2019, and December 2022 were screened for rotavirus group A (RVA), norovirus genogroup II (GII), astrovirus, sapovirus, and adenovirus type F40/41 using realtime reverse transcription polymerase chain reaction. The proportions positive across the four years were compared using the chi-squared test statistic. Results One or more of the five virus targets were detected in 282 (32.4%) cases. A reduction in the positivity rate of RVA cases was observed from 2019 (12.1%, 95% confidence interval (CI) 8.7% to 16.2%) to 2020 (1.7%, 95% CI 0.2% to 6.0%; p < 0.001). However, in the 2022, RVA positivity rate rebounded to 23.5% (95% CI 18.2% to 29.4%). For norovirus GII, the positivity rate fluctuated over the four years with its highest positivity rate observed in 2020 (16.2%; 95% C.I, 10.0% to 24.1%). No astrovirus cases were detected in 2020 and 2021, but the positivity rate in 2022 was similar to that in 2019 (3.1% (95% CI 1.5% to 5.7%) vs 3.3% (95% CI 1.4% to 6.5%)). A higher case fatality rate was observed in 2021 (9.0%) compared to the 2019 (3.2%), 2020 (6.8%) and 2022 (2.1%) (p <0.001). Conclusion Our study finds that in 2020 the transmission of common enteric viruses, especially RVA and astrovirus, in Kilifi Kenya may have been disrupted due to the COVID_19 NPIs. After 2020, local enteric virus transmission patterns appeared to return to prepandemic levels coinciding with the removal of most of the government COVID_19 NPIs.

Genomic epidemiology of SARS-CoV-2 within households in coastal Kenya: a case ascertained cohort study (preprint)

Background Analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomic sequence data from household infections should aid its detailed epidemiological understanding. Using viral genomic sequence data, we investigated household SARS-CoV-2 transmission and evolution in coastal Kenya households. Methods We conducted a case-ascertained cohort study between December 2020 and February 2022 whereby 573 members of 158 households were prospectively monitored for SARS-CoV-2 infection. Households were invited to participate if a member tested SARS-CoV-2 positive or was a contact of a confirmed case. Follow-up visits collected a nasopharyngeal/oropharyngeal (NP/OP) swab on days 1, 4 and 7 for RT-PCR diagnosis. If any of these were positive, further swabs were collected on days 10, 14, 21 and 28. Positive samples with an RT-PCR cycle threshold of <33.0 were subjected to whole genome sequencing followed by phylogenetic analysis. Ancestral state reconstruction was used to determine if multiple viruses had entered households. Results Of 2,091 NP/OP swabs that were collected, 375 (17.9%) tested SARS-CoV-2 positive. Viral genome sequences (>80% coverage) were obtained from 208 (55%) positive samples obtained from 61 study households. These genomes fell within 11 Pango lineages and four variants of concern (Alpha, Beta, Delta and Omicron). We estimated 163 putative transmission events involving members of the sequenced households, 40 (25%) of which were intra-household transmission events while 123 (75%) were infections that likely occurred outside the households. Multiple virus introductions (up-to-5) were observed in 28 (47%) households with the 1-month follow-up period. Conclusions We show that a considerable proportion of SARS-CoV-2 infections in coastal Kenya occurred outside the household setting. Multiple virus introductions frequently occurred into households within the same infection wave in contrast to observations from high income settings, where single introduction appears to be the norm. Our findings suggests that control of SARS-CoV-2 transmission by household member isolation may be impractical in this setting.

The genomic epidemiology of SARS-CoV-2 variants of concern in Kenya (preprint)

The emergence and establishment of SARS CoV 2 variants of concern presented a major global public health crisis across the world. There were six waves of SARS CoV 2 cases in Kenya that corresponded with the introduction and eventual dominance of the major SARS-COV-2 variants of concern, excepting the first 2 waves that were both wildtype virus. We estimate that more than 1000 SARS CoV 2 introductions occurred in the two-year epidemic period (March 2020 to September 2022) and a total of 930 introductions were associated with variants of concern namely Beta (n=78), Alpha(n=108), Delta(n=239) and Omicron (n=505). A total of 29 introductions were associated with A.23.1 variant that circulated in high frequencies in Uganda and Rwanda. The actual number of introductions is likely to be higher than these conservative estimates due to limited genomic sequencing. Our data suggested that cryptic transmission was usually underway prior to the first real-time identification of a new variant, and that multiple introductions were responsible. Following emergence of each VOC and subsequent introduction, transmission patterns were associated with hotspots of transmission in Coast, Nairobi and Western Kenya and follows established land and air transport corridors. Understanding the introduction and dispersal of major circulating variants and identifying the sources of new introductions is important to inform public health control strategies within Kenya and the larger East-African region. Border control and case finding reactive to new variants is unlikely to be a successful control strategy.

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance. (preprint)

Investment in Africa over the past year with regards to SARS-CoV-2 genotyping has led to a massive increase in the number of sequences, exceeding 100,000 genomes generated to track the pandemic on the continent. Our results show an increase in the number of African countries able to sequence within their own borders, coupled with a decrease in sequencing turnaround time. Findings from this genomic surveillance underscores the heterogeneous nature of the pandemic but we observe repeated dissemination of SARS-CoV-2 variants within the continent. Sustained investment for genomic surveillance in Africa is needed as the virus continues to evolve, particularly in the low vaccination landscape. These investments are very crucial for preparedness and response for future pathogen outbreaks.

A computational framework for modelling infectious disease policy based on age and household structure with applications to the COVID-19 pandemic (preprint)

The widespread, and in many countries unprecedented, use of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic has highlighted the need for mathematical models which can estimate the impact of these measures while accounting for the highly heterogeneous risk profile of COVID-19. Models accounting either for age structure or the household structure necessary to explicitly model many NPIs are commonly used in infectious disease modelling, but models incorporating both levels of structure present substantial computational and mathematical challenges due to their high dimensionality. Here we present a modelling framework for the spread of an epidemic that includes explicit representation of age structure and household structure. Our model is formulated in terms of tractable systems of ordinary differential equations for which we provide an open-source Python implementation. Such tractability leads to significant benefits for model calibration, exhaustive evaluation of possible parameter values, and interpretability of results. We demonstrate the flexibility of our model through four policy case studies, where we quantify the likely benefits of the following measures which were either considered or implemented in the UK during the current COVID-19 pandemic: control of within- and between-household mixing through NPIs; formation of support bubbles during lockdown periods; out-of-household isolation (OOHI); and temporary relaxation of NPIs during holiday periods. Our ordinary differential equation formulation and associated analysis demonstrate that multiple dimensions of risk stratification and social structure can be incorporated into infectious disease models without sacrificing mathematical tractability. This model and its software implementation expand the range of tools available to infectious disease policy analysts.

Genomic surveillance reveals the spread patterns of SARS-CoV-2 in coastal Kenya during the first two waves (preprint)

The transmission networks of SARS-CoV-2 in sub-Saharan Africa remain poorly understood. We analyzed 684 genomes from samples collected across six counties in coastal Kenya during the first two waves (March 2020 - February 2021). Up to 32 Pango lineages were detected in the local sample with six accounting for 88.0% of the sequenced infections: B.1 (60.4%), B.1.1 (8.9%), B.1.549 (7.9%), B.1.530 (6.4%), N.8 (4.4%) and A (3.1%). In a contemporaneous global sample, 571 lineages were identified, 247 for Africa and 88 for East Africa. We detected 262 location transition events comprising: 64 viral imports into Coastal Kenya; 26 viral exports from coastal Kenya; and 172 inter-county import/export events. Most international viral imports (61%) and exports (88%) occurred through Mombasa, a key coastal touristic and commercial center; and many occurred prior to June 2020, when stringent local COVID-19 restriction measures were enforced. After this period, local transmission dominated, and distinct local phylogenies were seen. Our analysis supports moving control strategies from a focus on international travel to local transmission.

Improving SARS-CoV-2 cumulative incidence estimation through mixture modelling of antibody levels (preprint)

As countries decide on vaccination strategies and how to ease movement restrictions, estimates of cumulative incidence of SARS-CoV-2 infection are essential in quantifying the extent to which populations remain susceptible to COVID-19. Cumulative incidence is usually estimated from seroprevalence data, where seropositives are defined by an arbitrary threshold antibody level, and adjusted for sensitivity and specificity at that threshold. This does not account for antibody waning nor for lower antibody levels in asymptomatic or mildly symptomatic cases. Mixture modelling can estimate cumulative incidence from antibody-level distributions without requiring adjustment for sensitivity and specificity. To illustrate the bias in standard threshold-based seroprevalence estimates, we compared both approaches using data from several Kenyan serosurveys. Compared to the mixture model estimate, threshold analysis underestimated cumulative incidence by 31% (IQR: 11 to 41) on average. Until more discriminating assays are available, mixture modelling offers an approach to reduce bias in estimates of cumulative incidence.

Spatially resolved simulations of the spread of COVID-19 in European countries (preprint)

We explore the spatial and temporal spread of the novel SARS-CoV-2 virus under containment measures in three European countries based on fits to data of the early outbreak. Using data from Spain and Italy, we estimate an age dependent infection fatality ratio for SARS-CoV-2, as well as risks of hospitalization and intensive care admission. We use them in a model that simulates the dynamics of the virus using an age structured, spatially detailed agent based approach, that explicitly incorporates governamental interventions, changes in mobility and contact patterns occurred during the COVID-19 outbreak in each country.Our simulations reproduce several of the features of its spatio-temporal spread in the three countries studied. They show that containment measures combined with high density are responsible for the containment of cases within densely populated areas, and that spread to less densely populated areas occurred during the late stages of the first wave. The capability to reproduce observed features of the spatio-temporal dynamics of SARS-CoV-2 makes this model a potential candidate for forecasting the dynamics of SARS-CoV-2 in other settings, and we recommend its application in low and lower-middle countries which remain understudied.

Tracking the introduction and spread of SARS-CoV-2 in coastal Kenya (preprint)

We generated 274 SARS-CoV-2 genomes from samples collected during the early phase of the Kenyan pandemic. Phylogenetic analysis identified 8 global lineages and at least 76 independent SARS-CoV-2 introductions into Kenyan coast. The dominant B.1 lineage (European origin) accounted for 82.1% of the cases. Lineages A, B and B.4 were detected from screened individuals at the Kenya-Tanzania border or returning travellers but did not lead to established transmission. Though multiple lineages were introduced in coastal Kenya within three months following the initial confirmed case, none showed extensive local expansion other than cases characterised by lineage B.1, which accounted for 45 of the 76 introductions. We conclude that the international points of entry were important conduits of SARS-CoV-2 importations. We speculate that early public health responses prevented many introductions leading to established transmission, but nevertheless a few undetected introductions were sufficient to give rise to an established epidemic.

Tracking the introduction and spread of SARS-CoV-2 in coastal Kenya (preprint)

We generated 274 SARS-CoV-2 genomes from samples collected during the early phase of the Kenyan pandemic. Phylogenetic analysis identified 8 global lineages and at least 76 independent SARS-CoV-2 introductions into Kenyan coast. The dominant B.1 lineage (European origin) accounted for 82.1% of the cases. Lineages A, B and B.4 were detected from screened individuals at the Kenya-Tanzania border or returning travellers but did not lead to established transmission. Though multiple lineages were introduced in coastal Kenya within three months following the initial confirmed case, none showed extensive local expansion other than cases characterised by lineage B.1, which accounted for 45 of the 76 introductions. We conclude that the international points of entry were important conduits of SARS-CoV-2 importations. We speculate that early public health responses prevented many introductions leading to established transmission, but nevertheless a few undetected introductions were sufficient to give rise to an established epidemic.

MAJORA: Continuous integration supporting decentralised sequencing for SARS-CoV-2 genomic surveillance (preprint)

Genomic epidemiology has become an increasingly common tool for epidemic response. Recent technological advances have made it possible to sequence genomes rapidly enough to inform outbreak response, and cheaply enough to justify dense sampling of even large epidemics. With increased availability of sequencing it is possible for agile networks of sequencing facilities to collaborate on the sequencing and analysis of epidemic genomic data. In response to the ongoing SARS-CoV-2 pandemic in the United Kingdom, the COVID-19 Genomics UK (COG-UK) consortium was formed with the aim of rapidly sequencing SARS-CoV-2 genomes as part of a national-scale genomic surveillance strategy. The network consists of universities, academic institutes, regional sequencing centres and the four UK Public Health Agencies. We describe the development and deployment of Majora, an encompassing digital infrastructure to address the challenge of collecting and integrating both genomic sequencing data and sample-associated metadata produced across the COG-UK network. The system was designed and implemented pragmatically to stand up capacity rapidly in a pandemic caused by a novel virus. This approach has underpinned the success of COG-UK, which has rapidly become the leading contributor of SARS-CoV-2 genomes to international databases and has generated over 60,000 sequences to date.

Revealing the extent of the COVID-19 pandemic in Kenya based on serological and PCR-test data (preprint)

Policy makers in Africa need robust estimates of the current and future spread of SARS-CoV-2. Data suitable for this purpose are scant. We used national surveillance PCR test, serological survey and mobility data to develop and fit a county-specific transmission model for Kenya. We estimate that the SARS-CoV-2 pandemic peaked before the end of July 2020 in the major urban counties, with 34 - 41% of residents infected, and will peak elsewhere in the country within 2-3 months. Despite this penetration, reported severe cases and deaths are low. Our analysis suggests the COVID-19 disease burden in Kenya may be far less than initially feared. A similar scenario across sub-Saharan Africa would have implications for balancing the consequences of restrictions with those of COVID-19.

The importance of supplementary immunisation activities to prevent measles outbreaks during the COVID-19 pandemic in Kenya (preprint)

BackgroundThe COVID-19 pandemic has disrupted routine measles immunisation and supplementary immunisation activities (SIAs) in most countries including Kenya. We assessed the risk of measles outbreaks during the pandemic in Kenya as a case study for the African Region. MethodsCombining measles serological data, local contact patterns, and vaccination coverage into a cohort model, we predicted the age-adjusted population immunity in Kenya and estimated the probability of outbreaks when contact-reducing COVID-19 interventions are lifted. We considered various scenarios for reduced measles vaccination coverage from April 2020. FindingsIn February 2020, when a scheduled SIA was postponed, population immunity was close to the herd immunity threshold and the probability of a large outbreak was 22% (0-46). As the COVID-19 restrictions to physical contact are lifted, from December 2020, the probability of a large measles outbreak increased to 31% (8-51), 35% (16-52) and 43% (31-56) assuming a 15%, 50% and 100% reduction in measles vaccination coverage. By December 2021, this risk increases further to 37% (17-54), 44% (29-57) and 57% (48-65) for the same coverage scenarios respectively. However, the increased risk of a measles outbreak following the lifting of restrictions on contact can be overcome by conducting an SIA with [≥] 95% coverage in under-fives. InterpretationWhile contact restrictions sufficient for SAR-CoV-2 control temporarily reduce measles transmissibility and the risk of an outbreak from a measles immunity gap, this risk rises rapidly once physical distancing is relaxed. Implementing delayed SIAs will be critical for prevention of measles outbreaks once contact restrictions are fully lifted in Kenya. FundingThe United Kingdoms Medical Research Council and the Department for International Development

Forecasting the scale of the COVID-19 epidemic in Kenya (preprint)

Background The first COVID-19 case in Kenya was confirmed on March 13th, 2020. Here, we provide forecasts for the potential incidence rate, and magnitude, of a COVID-19 epidemic in Kenya based on the observed growth rate and age distribution of confirmed COVID-19 cases observed in China, whilst accounting for the demographic and geographic dissimilarities between China and Kenya. Methods We developed a modelling framework to simulate SARS-CoV-2 transmission in Kenya, KenyaCoV. KenyaCoV was used to simulate SARS-CoV-2 transmission both within, and between, different Kenyan regions and age groups. KenyaCoV was parameterized using a combination of human mobility data between the defined regions, the recent 2019 Kenyan census, and estimates of age group social interaction rates specific to Kenya. Key epidemiological characteristics such as the basic reproductive number and the age-specific rate of developing COVID-19 symptoms after infection with SARS-CoV-2, were adapted for the Kenyan setting from a combination of published estimates and analysis of the age distribution of cases observed in the Chinese outbreak. Results We find that if person-to-person transmission becomes established within Kenya, identifying the role of subclinical, and therefore largely undetected, infected individuals is critical to predicting and containing a very significant epidemic. Depending on the transmission scenario our reproductive number estimates for Kenya range from 1.78 (95% CI 1.44 - 2.14) to 3.46 (95% CI 2.81-4.17). In scenarios where asymptomatic infected individuals are transmitting significantly, we expect a rapidly growing epidemic which cannot be contained only by case isolation. In these scenarios, there is potential for a very high percentage of the population becoming infected (median estimates: >80% over six months), and a significant epidemic of symptomatic COVID-19 cases. Exceptional social distancing measures can slow transmission, flattening the epidemic curve, but the risk of epidemic rebound after lifting restrictions is predicted to be high.