Characterising transmission parameters of SARS-CoV-2 in a peri-urban setting in Mozambique using population-based surveillance and a high-throughput sero-assay

Study registration

Clinicaltrials.gov: NCT04442165 https://clinicaltrials.gov/study/NCT04442165

Participating organisations

• Instituut voor Tropische Geneeskunde (ITM), Belgium (Coordinating PI Brecht Ingelbeen, former: Marc-Alain Widdowson)
• Instituto Nacional de Saúde (INS), Mozambique (PI Ivalda Macicame)
• Institut de Recherche pour le Développement (IRD), France (PI Martine Peeters)

Project summary

During December 2020-March 2022, households of a population cohort embedded in the Demographic Health Surveillance System (HDSS) of Polana Caniço, Maputo, Mozambique (16,500 people in a peri-urban neighbourhood of Maputo) were contacted biweekly. Residents reporting any respiratory sign, anosmia, or ageusia, were asked to self-administer a nasal swab, for SARS-CoV-2 PCR testing. Of a subset of participants, dried blood spots were repeatedly collected three-monthly from finger pricks at home. Antibodies against SARS-CoV-2 spike glycoprotein and nucleocapsid protein were detected using an in-house developed multiplex antibody assay. We estimated the incidence of respiratory illness and COVID-19, and SARS-CoV-2 seroprevalence. We used Cox regression models, adjusting for age and sex, to identify factors associated with first symptomatic COVID-19 and with SARS-CoV-2 sero-conversion in the first six months.

Timeline

Data and sample collection: 15/12/2020 to 31/03/2022 (data completion up to 30/04/2022)

Manuscripts

• Evaluation of a surrogate virus neutralization test for high-throughput serosurveillance of SARS-CoV-2 https://doi.org/10.1016/j.jviromet.2021.114228
• Mild and moderate COVID-19 during Alpha, Delta and Omikron pandemic waves in urban Maputo, Mozambique, December 2020-March 2022: a population-based surveillance study https://doi.org/10.1101/2023.12.22.23300474

Study participants

• Number of households in the active surveillance component (biweekly visits to detect possible Covid-19 cases): 1561
• Number of individuals in the active surveillance component: 6049
• Number of person-years followed up in the active surveillance: 1895.9
• Number of individuals in the repeated sero-survey (with >/=1 DBS collected): 1412
• Number of individuals in the social mixing survey: (to be completed)

Data

Three dataframes in csv format used for results of https://doi.org/10.1101/2023.12.22.23300474. To ensure confidentiality, the open data does not contain geographical coordinates nor any other demographic data (e.g. age, household structure) that could allow identification of participants.

1. possible COVID-19 case data (possible cases identified during household visits, with SARS-CoV-2 PCR test results, clinical signs and symptoms, age groups, sex, and socio-economic quintile): "possiblecases_pseudo.csv"
2. active COVID-19 survceillance data (all participants with demographic and baseline participant and household data, the number of household visits (follow-up time), and if reported, SARS-CoV-2 PCR test results (events)): "cases_participantsFU_pseudo.csv"
3. sero-survey data (dried blood spots collected 15 December 2020-31 July 2021 with demographic and baseline participant data): "serosurvey_pseudo.csv"

Data analysis scripts

Script are in R. Analyses can be run on the open anonymized dataframes available on this repository.

1. "/symptoms_associated_with_COVID19.R" the code to analyse clinical signs and symptoms associated with COVID-19 confirmation (SARS-CoV-2 PCR positive) among possible COVID-19 cases (onset of at least one respiratory symptom, ageusia, or anosmia in the past 2 weeks). It can be run on the "possiblecases_pseudo.csv" data.
2. "/disease_incidence.R" the code to analyse active household follow-up: describe cohort participant characteristics, epidemiological curve, COVID-19 risk factor analysis (Cox regression). Part of it can be run on the "participant_cases_FU.csv" data. Part of it uses identifying data (e.g., geographical coordinates), which is not available as open data.
3. "/infection_prevalence_risk_factors.R" the code to analyse the serosurvey results: describe serosurvey participant characteristics, samples collected, sero-prevalence over time, and SARS-CoV-2 seroconversion risk factor analysis (Cox regression). It can be run on the "serosurvey_pseudo.csv" data.