Symptom prevalence and secondary attack rate of SARS-CoV-2 in rural Kenyan households: A prospective cohort study.

BACKGROUND: We estimated the secondary attack rate of SARS-CoV-2 among household contacts of PCR-confirmed cases of COVID-19 in rural Kenya and analysed risk factors for transmission. METHODS: We enrolled incident PCR-confirmed cases and their household members. At baseline, a questionnaire, a blood sample, and naso-oropharyngeal swabs were collected. Household members were followed 4, 7, 10, 14, 21 and 28 days after the date of the first PCR-positive in the household; naso-oropharyngeal swabs were collected at each visit and used to define secondary cases. Blood samples were collected every 1-2 weeks. Symptoms were collected in a daily symptom diary. We used binomial regression to estimate secondary attack rates and survival analysis to analyse risk factors for transmission. RESULTS: A total of 119 households with at least one positive household member were enrolled between October 2020 and September 2022, comprising 503 household members; 226 remained in follow-up at day 14 (45%). A total of 43 secondary cases arose within 14 days of identification of the primary case, and 81 household members remained negative. The 7-day secondary attack rate was 4% (95% CI 1%-10%), the 14-day secondary attack rate was 28% (95% CI 17%-40%). Of 38 secondary cases with data, eight reported symptoms (21%, 95% CI 8%-34%). Antibody to SARS-CoV-2 spike protein at enrolment was not associated with risk of becoming a secondary case. CONCLUSION: Households in our setting experienced a lower 7-day attack rate than a recent meta-analysis indicated as the global average (23%-43% depending on variant), and infection is mostly asymptomatic in our setting.

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

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.

Infection patterns of endemic human coronaviruses in rural households in coastal Kenya.

Background: The natural history and transmission patterns of endemic human coronaviruses are of increased interest following the emergence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Methods: In rural Kenya 483 individuals from 47 households were followed for six months (2009-10) with nasopharyngeal swabs collected twice weekly regardless of symptoms. A total of 16,918 swabs were tested for human coronavirus (hCoV) OC43, NL63 and 229E and other respiratory viruses using polymerase chain reaction. Results: From 346 (71.6%) household members, 629 hCoV infection episodes were defined, with 36.3% being symptomatic: varying by hCoV type and decreasing with age. Symptomatic episodes (aHR=0.6 (95% CI:0.5-0.8) or those with elevated peak viral load (medium aHR=0.4 (0.3-0.6); high aHR=0.31 (0.2-0.4)) had longer viral shedding compared to their respective counterparts. Homologous reinfections were observed in 99 (19.9%) of 497 first infections. School-age children (55%) were the most common index cases with those having medium (aOR=5.3 (2.3 - 12.0)) or high (8.1 (2.9 - 22.5)) peak viral load most often generating secondary cases. Conclusion: Household coronavirus infection was common, frequently asymptomatic and mostly introduced by school-age children. Secondary transmission was influenced by viral load of index cases. Homologous-type reinfection was common. These data may be insightful for SARS-CoV-2.

COVID-19 transmission dynamics underlying epidemic waves in Kenya.

Policy decisions on COVID-19 interventions should be informed by a local, regional and national understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission. Epidemic waves may result when restrictions are lifted or poorly adhered to, variants with new phenotypic properties successfully invade, or infection spreads to susceptible subpopulations. Three COVID-19 epidemic waves have been observed in Kenya. Using a mechanistic mathematical model, we explain the first two distinct waves by differences in contact rates in high and low social-economic groups, and the third wave by the introduction of higher-transmissibility variants. Reopening schools led to a minor increase in transmission between the second and third waves. Socioeconomic and urban­rural population structure are critical determinants of viral transmission in Kenya.

Integrating epidemiological and genetic data with different sampling intensities into a dynamic model of respiratory syncytial virus transmission.

Respiratory syncytial virus (RSV) is responsible for a significant burden of severe acute lower respiratory tract illness in children under 5 years old; particularly infants. Prior to rolling out any vaccination program, identification of the source of infant infections could further guide vaccination strategies. We extended a dynamic model calibrated at the individual host level initially fit to social-temporal data on shedding patterns to include whole genome sequencing data available at a lower sampling intensity. The study population was 493 individuals (55 aged < 1 year) distributed across 47 households, observed through one RSV season in coastal Kenya. We found that 58/97 (60%) of RSV-A and 65/125 (52%) of RSV-B cases arose from infection probably occurring within the household. Nineteen (45%) infant infections appeared to be the result of infection by other household members, of which 13 (68%) were a result of transmission from a household co-occupant aged between 2 and 13 years. The applicability of genomic data in studies of transmission dynamics is highly context specific; influenced by the question, data collection protocols and pathogen under investigation. The results further highlight the importance of pre-school and school-aged children in RSV transmission, particularly the role they play in directly infecting the household infant. These age groups are a potential RSV vaccination target group.

Revealing the extent of the first wave of the COVID-19 pandemic in Kenya based on serological and PCR-test data.

Policymakers in Africa need robust estimates of the current and future spread of SARS-CoV-2. We used national surveillance PCR test, serological survey and mobility data to develop and fit a county-specific transmission model for Kenya up to the end of September 2020, which encompasses the first wave of SARS-CoV-2 transmission in the country. We estimate that the first wave of the SARS-CoV-2 pandemic peaked before the end of July 2020 in the major urban counties, with 30-50% of residents infected. Our analysis suggests, first, that the reported low COVID-19 disease burden in Kenya cannot be explained solely by limited spread of the virus, and second, that a 30-50% attack rate was not sufficient to avoid a further wave of transmission.

Model-based estimates of transmission of respiratory syncytial virus within households.

INTRODUCTION: Respiratory syncytial virus (RSV) causes a significant respiratory disease burden in the under 5 population. The transmission pathway to young children is not fully quantified in low-income settings, and this information is required to design interventions. METHODS: We used an individual level transmission model to infer transmission parameters using data collected from 493 individuals distributed across 47 households over a period of 6 months spanning the 2009/2010 RSV season. A total of 208 episodes of RSV were observed from 179 individuals. We model competing transmission risk from within household exposure and community exposure while making a distinction between RSV groups A and B. RESULTS: We find that 32-53% of all RSV transmissions are between members of the same household; the rate of pair-wise transmission is 58% (95% CrI: 30-74%) lower in larger households (≥8 occupants) than smaller households; symptomatic individuals are 2-7 times more infectious than asymptomatic individuals i.e. 2.48 (95% CrI: 1.22-5.57) among symptomatic individuals with low viral load and 6.7(95% CrI: 2.56-16) among symptomatic individuals with high viral load; previous infection reduces susceptibility to re-infection within the same epidemic by 47% (95% CrI: 17%-68%) for homologous RSV group and 39% (95%CrI: -8%-69%) for heterologous group; RSV B is more frequently introduced into the household, and RSV A is more rapidly transmitted once in the household. DISCUSSION: Our analysis presents the first transmission modelling of cohort data for RSV and we find that it is important to consider the household social structuring and household size when modelling transmission. The increased infectiousness of symptomatic individuals implies that a vaccine against RSV related disease would also have an impact on infection transmission. Together, the weak cross immunity between RSV groups and the possibility of different transmission niches could form part of the explanation for the group co-existence.

Transmission patterns and evolution of respiratory syncytial virus in a community outbreak identified by genomic analysis.

Detailed information on the source, spread and evolution of respiratory syncytial virus (RSV) during seasonal community outbreaks remains sparse. Molecular analyses of attachment (G) gene sequences from hospitalized cases suggest that multiple genotypes and variants co-circulate during epidemics and that RSV persistence over successive seasons is characterized by replacement and multiple new introductions of variants. No studies have defined the patterns of introduction, spread and evolution of RSV at the local community and household level. We present a whole genome sequence analysis of 131 RSV group A viruses collected during 6-month household-based RSV infection surveillance in Coastal Kenya, 2010 within an area of 12 km2. RSV infections were identified by regular symptom-independent screening of all household members twice weekly. Phylogenetic analysis revealed that the RSV A viruses in nine households were closely related to genotype GA2 and fell within a single branch of the global phylogeny. Genomic analysis allowed the detection of household-specific variation in seven households. For comparison, using only G gene analysis, household-specific variation was found only in one of the nine households. Nucleotide changes were observed both intra-host (viruses identified from same individual in follow-up sampling) and inter-host (viruses identified from different household members) and these coupled with sampling dates enabled a partial reconstruction of the within household transmission chains. The genomic evolutionary rate for the household dataset was estimated as 2.307 × 10 - 3 (95% highest posterior density: 0.935-4.165× 10 - 3) substitutions/site/year. We conclude that (i) at the household level, most RSV infections arise from the introduction of a single virus variant followed by accumulation of household specific variation and (ii) analysis of complete virus genomes is crucial to better understand viral transmission in the community. A key question arising is whether prevention of RSV introduction or spread within the household by vaccinating key transmitting household members would lead to a reduced onward community-wide transmission.

Defining the vaccination window for respiratory syncytial virus (RSV) using age-seroprevalence data for children in Kilifi, Kenya.

BACKGROUND: Respiratory syncytial virus (RSV) is an important cause of lower respiratory tract disease in early life and a target for vaccine prevention. Data on the age-prevalence of RSV specific antibodies will inform on optimizing vaccine delivery. METHODS: Archived plasma samples were randomly selected within age strata from 960 children less than 145 months of age admitted to Kilifi County Hospital pediatric wards between 2007 and 2010. Samples were tested for antibodies to RSV using crude virus IgG ELISA. Seroprevalence (and 95% confidence intervals) was estimated as the proportion of children with specific antibodies above a defined cut-off level. Nested catalytic models were used to explore different assumptions on antibody dynamics and estimate the rates of decay of RSV specific maternal antibody and acquisition of infection with age, and the average age of infection. RESULTS: RSV specific antibody prevalence was 100% at age 0-<1month, declining rapidly over the first 6 months of life, followed by an increase in the second half of the first year of life and beyond. Seroprevalence was lowest throughout the age range 5-11 months; all children were seropositive beyond 3 years of age. The best fit model to the data yielded estimates for the rate of infection of 0.78/person/year (95% CI 0.65-0.97) and 1.69/person/year (95% CI 1.27-2.04) for ages 0-<1 year and 1-<12 years, respectively. The rate of loss of maternal antibodies was estimated as 2.54/year (95% CI 2.30-2.90), i.e. mean duration 4.7 months. The mean age at primary infection was estimated at 15 months (95% CI 13-18). CONCLUSIONS: The rate of decay of maternal antibody prevalence and subsequent age-acquisition of infection are rapid, and the average age at primary infection early. The vaccination window is narrow, and suggests optimal targeting of vaccine to infants 5 months and above to achieve high seroconversion.