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Supplementary Material 1

INTRODUCTION: Cholera outbreaks in fragile settings are prone to rapid expansion. Case-area targeted interventions (CATIs) have been proposed as a rapid and efficient response strategy to halt or substantially reduce the size of small outbreaks. CATI aims to deliver synergistic interventions (eg, water, sanitation, and hygiene interventions, vaccination, and antibiotic chemoprophylaxis) to households in a 100-250 m 'ring' around primary outbreak cases. METHODS AND ANALYSIS: We report on a protocol for a prospective observational study of the effectiveness of CATI. Médecins Sans Frontières (MSF) plans to implement CATI in the Democratic Republic of the Congo (DRC), Cameroon, Niger and Zimbabwe. This study will run in parallel to each implementation. The primary outcome is the cumulative incidence of cholera in each CATI ring. CATI will be triggered immediately on notification of a case in a new area. As with most real-world interventions, there will be delays to response as the strategy is rolled out. We will compare the cumulative incidence among rings as a function of response delay, as a proxy for performance. Cross-sectional household surveys will measure population-based coverage. Cohort studies will measure effects on reducing incidence among household contacts and changes in antimicrobial resistance. ETHICS AND DISSEMINATION: The ethics review boards of MSF and the London School of Hygiene and Tropical Medicine have approved a generic protocol. The DRC and Niger-specific versions have been approved by the respective national ethics review boards. Approvals are in process for Cameroon and Zimbabwe. The study findings will be disseminated to the networks of national cholera control actors and the Global Task Force for Cholera Control using meetings and policy briefs, to the scientific community using journal articles, and to communities via community meetings.

Additional file 1. Search terms, and search strategy and results by database.

Additional file 1: Additional details on specific analysis steps.

Additional file 1: Figure S5. Causal framework for acute malnutrition among children, used to identify potential predictors. Figure S6. GLM-predicted versus observed SAM (MUAC + oedema) prevalence, Somalia, by district-month, on training data, LOOCV and holdout data. Shaded channels indicate different absolute deviance of predictions. Vertical dotted lines denote commonly used SAM prevalence thresholds. Figure S7. GLM-predicted versus observed GAM (WFH + oedema) prevalence, Somalia, by district-month, on training data, LOOCV and holdout data. Shaded channels indicate different absolute deviance of predictions. Vertical dotted lines denote commonly used GAM prevalence thresholds. Figure S8. GLM-predicted versus observed GAM (MUAC + oedema) prevalence, Somalia, by district-month, on training data, LOOCV and holdout data. Shaded channels indicate different absolute deviance of predictions. Vertical dotted lines denote commonly used GAM prevalence thresholds. Figure S9. GLM-predicted versus observed mean WFH, Somalia, by district-month, on training data, LOOCV and holdout data. Shaded channels indicate different absolute deviance of predictions. Vertical dotted lines denote potentially useful thresholds. Figure S10. GLM-predicted versus observed mean MUAC, Somalia, by district-month, on training data, LOOCV and holdout data. Shaded channels indicate different absolute deviance of predictions. Vertical dotted lines denote potentially useful thresholds. Table S7. Performance of random forest models in Somalia, by acute malnutrition outcome. Figure S11. RF-predicted versus observed GAM (WFH + oedema) prevalence, Somalia, by district-month, on training data, LOOCV and holdout data. Shaded channels indicate different absolute deviance of predictions. Vertical dotted lines denote commonly used GAM prevalence thresholds. Figure S12. RF-predicted versus observed mean WFH, Somalia, by district-month, on training data, LOOCV and holdout data. Shaded channels indicate different absolute deviance of predictions. Vertical dotted lines denote potentially useful thresholds. Figure S13. GLM-predicted versus observed SAM (MUAC + oedema) prevalence, South Sudan, by district-month, on training data, LOOCV and holdout data. Shaded channels indicate different absolute deviance of predictions. Vertical dotted lines denote commonly used SAM prevalence thresholds. Figure S14. GLM-predicted versus observed GAM (WFH + oedema) prevalence, South Sudan, by district-month, on training data, LOOCV and holdout data. Shaded channels indicate different absolute deviance of predictions. Vertical dotted lines denote commonly used GAM prevalence thresholds. Figure S15. GLM-predicted versus observed GAM (MUAC + oedema) prevalence, South Sudan, by district-month, on training data, LOOCV and holdout data. Shaded channels indicate different absolute deviance of predictions. Vertical dotted lines denote commonly used GAM prevalence thresholds. Figure S16. GLM-predicted versus observed mean WFH, South Sudan, by district-month, on training data, LOOCV and holdout data. Shaded channels indicate different absolute deviance of predictions. Vertical dotted lines denote potentially useful thresholds. Figure S17. GLM-predicted versus observed mean MUAC, South Sudan, by district-month, on training data, LOOCV and holdout data. Shaded channels indicate different absolute deviance of predictions. Vertical dotted lines denote potentially useful thresholds. Table S8. Performance of random forest models in South Sudan, by acute malnutrition outcome. Figure S18. RF-predicted versus observed GAM (WFH + oedema) prevalence, South Sudan, by district-month, on training data, LOOCV and holdout data. Shaded channels indicate different absolute deviance of predictions. Vertical dotted lines denote commonly used GAM prevalence thresholds. Figure S19. RF-predicted versus observed mean WFH, South Sudan, by district-month, on training data, LOOCV and holdout data. Shaded channels indicate different absolute deviance of predictions. Vertical dotted lines denote potentially useful thresholds.

Supplementary figures and table of Koltai et al."Date of introduction and epidemiologic patterns of SARS-CoV-2 in Mogadishu, Somalia: estimates from transmission modelling of satellite-based excess mortality data in 2020".

Additional file 1. Questionnaire.