Increased Biting Rate of Insecticide-Resistant Culex Mosquitoes and Community Adherence to IRS for Malaria Control in Urban Malabo, Bioko Island, Equatorial Guinea.

Sustaining high levels of indoor residual spraying (IRS) coverage (≥85%) for community protection against malaria remains a challenge for IRS campaigns. We examined biting rates and insecticide resistance in Culex species and Anopheles gambiae s.l., and their potential effect on community adherence to IRS. The average IRS coverage in urban Malabo between 2015 and 2017 remained at 80%. Culex biting rate increased 6.0-fold (P < 0.001) between 2014 and 2017, reaching 8.08 bites per person per night, whereas that of An. gambiae s.l. remained steady at around 0.68. Although An. gambiae s.l. was susceptible to carbamates and organophosphates insecticides, Culex spp. were phenotypically resistant to all four main classes of WHO-recommended IRS insecticides. Similarly, the residual activity of the organophosphate insecticide used since 2017, ACTELLIC 300CS, was 8 mo for An. gambiae s.l., but was almost absent against Culex for 2 mo post-spray. A survey conducted in 2018 within urban Malabo indicated that 77.0% of respondents related IRS as means of protection against mosquito bites, but only 3.2% knew that only Anopheles mosquitoes transmit malaria. Therefore, the increasing biting rates of culicines in urban Malabo, and their resistance to all IRS insecticides, is raising concern that a growing number of people may refuse to participate in IRS as result of its perceived failure in controlling mosquitoes. Although this is not yet the case on Bioko Island, communication strategies need refining to sensitize communities about the effectiveness of IRS in controlling malaria vectors in the midst of insecticide resistance in nonmalaria vector mosquitoes

Mapping and enumerating houses and households to support malaria control interventions on Bioko Island.

BACKGROUND: Housing mapping and household enumeration are essential for the planning, implementation, targeting, and monitoring of malaria control interventions. In many malaria endemic countries, control efforts are hindered by incomplete or non-existent housing cartography and household enumeration. This paper describes the development of a comprehensive mapping and enumeration system to support the Bioko Island Malaria Control Project (BIMCP). RESULTS: A highly detailed database was developed to include every housing unit on Bioko Island and uniquely enumerate the associated households residing in these houses. First, the island was divided into a virtual, geo-dereferenced grid of 1 × 1 km sequentially numbered map-areas, each of which was in turn subdivided into one hundred, 100 × 100 m sequentially numbered map-sectors. Second, high-resolution satellite imagery was used to sequentially and uniquely identify all housing units within each map-sector. Third, where satellite imagery was not available, global positioning systems (GPS) were used as the basis for uniquely identifying and mapping housing units in a sequential manner. A total of 97,048 housing units were mapped by 2018, 56% of which were concentrated in just 5.2% of Bioko Island's total mapped area. Of these housing units, 70.7% were occupied, thus representing uniquely identified households. CONCLUSIONS: The housing unit mapping and household enumeration system developed for Bioko Island enabled the BIMCP to more effectively plan, implement, target, and monitor malaria control interventions. Since 2014, the BIMCP has used the unique household identifiers to monitor all household-level interventions, including indoor residual spraying, long-lasting insecticide-treated nets distribution, and annual malaria indicator surveys. The coding system used to create the unique housing unit and household identifiers is highly intuitive and allows quick location of any house within the grid without a GPS. Its flexibility has permitted the BIMCP to easily take into account the rapid and substantial changes in housing infrastructure. Importantly, by utilizing this coding system, an unprecedented quantity and diversity of detailed, geo-referenced demographic and health data have been assembled that have proved highly relevant for informing decision-making both for malaria control and potentially for the wider public health agenda on Bioko Island

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

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Correction: Development and evaluation of PlasmoPod: A cartridge-based nucleic acid amplification test for rapid malaria diagnosis and surveillance.

[This corrects the article DOI: 10.1371/journal.pgph.0001516.]

Development and evaluation of PlasmoPod: A cartridge-based nucleic acid amplification test for rapid malaria diagnosis and surveillance.

Malaria surveillance is hampered by the widespread use of diagnostic tests with low sensitivity. Adequate molecular malaria diagnostics are often only available in centralized laboratories. PlasmoPod is a novel cartridge-based nucleic acid amplification test for rapid, sensitive, and quantitative detection of malaria parasites. PlasmoPod is based on reverse-transcription quantitative polymerase chain reaction (RT-qPCR) of the highly abundant Plasmodium spp. 18S ribosomal RNA/DNA biomarker and is run on a portable qPCR instrument which allows diagnosis in less than 30 minutes. Our analytical performance evaluation indicates that a limit-of-detection as low as 0.02 parasites/μL can be achieved and no cross-reactivity with other pathogens common in malaria endemic regions was observed. In a cohort of 102 asymptomatic individuals from Bioko Island with low malaria parasite densities, PlasmoPod accurately detected 83 cases, resulting in an overall detection rate of 81.4%. Notably, there was a strong correlation between the Cq values obtained from the reference RT-qPCR assay and those obtained from PlasmoPod. In an independent cohort, using dried blood spots from malaria symptomatic children living in the Central African Republic, we demonstrated that PlasmoPod outperforms malaria rapid diagnostic tests based on the PfHRP2 and panLDH antigens as well as thick blood smear microscopy. Our data suggest that this 30-minute sample-to-result RT-qPCR procedure is likely to achieve a diagnostic performance comparable to a standard laboratory-based RT-qPCR setup. We believe that the PlasmoPod rapid NAAT could enable widespread accessibility of high-quality and cost-effective molecular malaria surveillance data through decentralization of testing and surveillance activities, especially in elimination settings

Real-time, spatial decision support to optimize malaria vector control: The case of indoor residual spraying on Bioko Island, Equatorial Guinea

Public health interventions require evidence-based decision-making to maximize impact. Spatial decision support systems (SDSS) are designed to collect, store, process and analyze data to generate knowledge and inform decisions. This paper discusses how the use of a SDSS, the Campaign Information Management System (CIMS), to support malaria control operations on Bioko Island has impacted key process indicators of indoor residual spraying (IRS): coverage, operational efficiency and productivity. We used data from the last five annual IRS rounds (2017 to 2021) to estimate these indicators. IRS coverage was calculated as the percentage of houses sprayed per unit area, represented by 100x100 m map-sectors. Optimal coverage was defined as between 80% and 85%, and under and overspraying as coverage below 80% and above 85%, respectively. Operational efficiency was defined as the fraction of map-sectors that achieved optimal coverage. Daily productivity was expressed as the number of houses sprayed per sprayer per day (h/s/d). These indicators were compared across the five rounds. Overall IRS coverage (i.e. percent of total houses sprayed against the overall denominator by round) was highest in 2017 (80.2%), yet this round showed the largest proportion of oversprayed map-sectors (36.0%). Conversely, despite producing a lower overall coverage (77.5%), the 2021 round showed the highest operational efficiency (37.7%) and the lowest proportion of oversprayed map-sectors (18.7%). In 2021, higher operational efficiency was also accompanied by marginally higher productivity. Productivity ranged from 3.3 h/s/d in 2020 to 3.9 h/s/d in 2021 (median 3.6 h/s/d). Our findings showed that the novel approach to data collection and processing proposed by the CIMS has significantly improved the operational efficiency of IRS on Bioko. High spatial granularity during planning and deployment together with closer follow-up of field teams using real-time data supported more homogeneous delivery of optimal coverage while sustaining high productivity. Author summary Effective public health interventions rely on high coverage to provide community protection. Coverage is determined by the proportion of a given target population that receives the intervention. The level of coverage required varies across settings and health problems. The question about how one achieves high coverage in an equitable manner is operationally challenging. Here, we describe the use of digital tools to support and optimize the delivery of a crucial and proven malaria control intervention, indoor residual spraying (IRS), on Bioko Island. We demonstrate that the scale at which one plans delivery and calculates coverage is critical for guaranteeing that the whole target population is served equally. We also show that achieving adequate high coverage during IRS implementation is challenging, but can be greatly supported by subdividing the target area into multiple, small area units and by using spatial decision support to guide deployment. We focused on IRS as a specific example, but the same digital tools can be used for other public health interventions, with an approach that promotes decision-making during implementation and allows better monitoring of intervention coverage, resulting in more efficient delivery

Real-time, spatial decision support to optimize malaria vector control: The case of indoor residual spraying on Bioko Island, Equatorial Guinea.

Public health interventions require evidence-based decision-making to maximize impact. Spatial decision support systems (SDSS) are designed to collect, store, process and analyze data to generate knowledge and inform decisions. This paper discusses how the use of a SDSS, the Campaign Information Management System (CIMS), to support malaria control operations on Bioko Island has impacted key process indicators of indoor residual spraying (IRS): coverage, operational efficiency and productivity. We used data from the last five annual IRS rounds (2017 to 2021) to estimate these indicators. IRS coverage was calculated as the percentage of houses sprayed per unit area, represented by 100x100 m map-sectors. Optimal coverage was defined as between 80% and 85%, and under and overspraying as coverage below 80% and above 85%, respectively. Operational efficiency was defined as the fraction of map-sectors that achieved optimal coverage. Daily productivity was expressed as the number of houses sprayed per sprayer per day (h/s/d). These indicators were compared across the five rounds. Overall IRS coverage (i.e. percent of total houses sprayed against the overall denominator by round) was highest in 2017 (80.2%), yet this round showed the largest proportion of oversprayed map-sectors (36.0%). Conversely, despite producing a lower overall coverage (77.5%), the 2021 round showed the highest operational efficiency (37.7%) and the lowest proportion of oversprayed map-sectors (18.7%). In 2021, higher operational efficiency was also accompanied by marginally higher productivity. Productivity ranged from 3.3 h/s/d in 2020 to 3.9 h/s/d in 2021 (median 3.6 h/s/d). Our findings showed that the novel approach to data collection and processing proposed by the CIMS has significantly improved the operational efficiency of IRS on Bioko. High spatial granularity during planning and deployment together with closer follow-up of field teams using real-time data supported more homogeneous delivery of optimal coverage while sustaining high productivity

Additional file 1 of Identifying individual, household and environmental risk factors for malaria infection on Bioko Island to inform interventions

Additional file 1: Table S1. Odds ratios estimated from regression models of all risk factors for malaria transmission on Bioko Island and 95% confidence intervals, stratified by rural and urban settings and malaria annual indicator survey year, 2015 and 2018

Analysis of asymptomatic malaria cohort.

(A) Correlation of of Cq valuesvales obtained from reference RT-qPCR run on the Biorad CFX96 instrumentintrument and PlasmoPod run on diaxxoPCR. Samples negative for PlasmoPod were assigned a Cq value of -1. (B) Detection probability for PlasmoPod modelled based on reference Cq values. The grey area represents the 95% confidence interval. (C) Detection rate of PlasmoPod stratified by age group. (D) Cq values stratified by age group.</p