DataSheet_1_Sustaining surveillance as an intervention during the COVID-19 pandemic in Cabo Verde and implications for malaria elimination.docx

Cabo Verde reported the first case of COVID-19 on March 19, 2020. Containment measures were quickly implemented and over 80,000 COVID-19 tests were performed in 2020 with 11,840 confirmed infections (2% of the population) and 154 deaths. In a setting where the last locally acquired malaria case was reported in January 2018, any interruptions to malaria care-seeking have the potential for infections to go untreated and transmission re-establishing. This work aims to determine whether there was any change in the number of people seeking care or being tested for malaria and, using an interrupted time series analysis, identify if any change was associated with implemented COVID-19 measures. Routinely collected surveillance data for outpatient visits, testing for malaria and COVID-19 were aggregated by month for each health facility (outpatient and malaria) or by municipality (COVID-19) from 2017 through 2020. The timeline of COVID-19 measures was generated based on when and where they were implemented. Results show that there was a marked shift in care-seeking in Cabo Verde. Overall, the mean number of observed outpatient visits decreased from 2,057 visits per month during 2017-2019 to 1,088 in 2020, an estimated 28% reduction. However, malaria testing rates per 1,000 outpatient visits after the pandemic began increased by 8% compared to expected trends. Results suggest that the pandemic impacted care-seeking but led to a non-significant increase in testing for malaria per 1,000 outpatient visits. With the cessation of international travel, the risk of imported infections seeding new transmission declined suggesting the risk of undetected transmission was low. It is important for countries to understand their specific malaria risks and vulnerabilities in order to ensure that any progress towards the interruption of malaria transmission can be sustained.</p

MOESM1 of Use of different transmission metrics to describe malaria epidemiology in the highlands of western Kenya

Additional file 1. Seroconversion rates (SCR) and corresponding 95 % confidence interval (CI) stratified by elevation and mosquito control categories. The table shows the seroconversion rates by elevation and mosquito control category, demonstrating lower exposure to malaria at altitudes above 1530 m and in households with both IRS and ITNs in their households

MOESM1 of Use of mobile technology-based participatory mapping approaches to geolocate health facility attendees for disease surveillance in low resource settings

Additional file 1. Example questionnaire and associated data types

Indoor densities of female anophelines by light trap in intervention and control clusters in Rachuonyo South District in March–August 2012.

<p>Each symbol represents the number of female anophelines caught indoors by CDC light trap inside hotspots (filled circles) and in evaluation zones (open circles). Each trap night, four compounds were randomly selected within the hotspot and eight were selected in the evaluation zone per cluster. Findings are summarized for trapping rounds prior to roll-out of interventions in 22 March–30 April 2012 (one trapping night per compound) and post-intervention in 1 May–30 June 2012 (three trapping nights per compound) and 1 July–31 August 2012 (five trapping nights per compound). Findings are presented for three intervention clusters combined and for three control clusters combined.</p

Spatial variation in malaria antibody prevalence and nPCR parasite prevalence in the study area in Rachuonyo South District during a community survey conducted in June–July 2011.

<p>(A) Distribution of sampled compounds and variations in altitude across the study site (contour interval = 25 m). (B) Combined seroprevalence (for AMA-1 or MSP-1₁₉) for individual 250 × 250 m sub-cells and the location of 27 significant hotspots derived from spatial scan analysis of compound-level data. (C) The ten hotspots that were selected for the cluster-randomized trail, presented with evaluation zones. (D) The 27 serological hotspot locations are overlaid on a map of nPCR-detected malaria parasite prevalence for compounds consisting of >3 individuals.</p

Cluster-randomized trial baseline characteristics (March–April 2012) and intervention coverage within control and intervention hotspots in Rachuonyo South District.

<p>Cluster-randomized trial baseline characteristics (March–April 2012) and intervention coverage within control and intervention hotspots in Rachuonyo South District.</p

The study area in the highlands of western Kenya.

<p>The study area comprised a 5 × 20 km rectangle in Rachuonyo South District, Nyanza Province.</p

Parasite prevalence and antibody prevalence in relation to age in the cross-sectional community survey in Rachuonyo South District in June–July 2011 that was conducted for hotspot detection.

<p>Parasite prevalence and antibody prevalence in relation to age in the cross-sectional community survey in Rachuonyo South District in June–July 2011 that was conducted for hotspot detection.</p

The impact of the combined targeted interventions on nPCR malaria parasite prevalence, complexity of infection, and allelic richness in hotspots and evaluation zones.

<p>The impact of the combined targeted interventions on nPCR malaria parasite prevalence, complexity of infection, and allelic richness in hotspots and evaluation zones.</p

Malaria parasite prevalence by nPCR inside and outside serologically defined hotspots in the study area in Rachuonyo South District during a community survey conducted in June–July 2011.

<p>nPCR-based parasite prevalence is plotted for individuals residing inside 27 serologically defined hotspots (black bars), 1–249 meters from the hotspot boundary (grey hatched bars), 250–500 m from the hotspot boundary (open hatched bars), and >500 meters from the hotspot boundary (open bars). Parasite prevalence by nPCR is shown per altitude band. Error bars indicate the upper limit of the 95% confidence interval; the <i>p</i>-value for the trend test is given, adjusting for correlations between observations from individuals living in the same compound. The number of individuals for whom samples were available for nPCR inside hotspot boundaries was 2,222 individuals (1,350–1,449 m), 2,494 (1,450–1,499 m), 1,348 (1,500–1,549 m), and 118 (1,550–1,650 m). The number of individuals for 1–249 m from hotspot boundaries was 698 (1,350–1,449 m), 1,248 (1,450–1,499 m), 1,113 (1,500–1,549 m), and 246 (1,550–1,650 m). The number of individuals for 250–500 m from hotspot boundaries was 544 (1,350–1,449 m), 681 (1,450–1,499 m), 661 (1,500–1,549 m), and 164 (1,550–1,650 m). The number of individuals for >500 m from hotspot boundaries was 544 (1,350–1,449 m), 176 (1,450–1,499 m), 405 (1,500–1,549 m), and 135 (1,550–1,650 m).</p