Monitoring mosquito nuisance for the development of a citizen science approach for malaria vector surveillance in Rwanda.

BACKGROUND: Many countries, including Rwanda, have mosquito monitoring programmes in place to support decision making in the fight against malaria. However, these programmes can be costly, and require technical (entomological) expertise. Involving citizens in data collection can greatly support such activities, but this has not yet been thoroughly investigated in a rural African context. METHODS: Prior to the implementation of such a citizen-science approach, a household entomological survey was conducted in October-November 2017 and repeated one year later in Busoro and Ruhuha sectors, in southern and eastern province of Rwanda, respectively. The goal was to evaluate the perception of mosquito nuisance reported by citizens as a potential indicator for malaria vector hotspots. Firstly, mosquito abundance and species composition were determined using Centers for Disease Control and Prevention (CDC) light traps inside the houses. Secondly, household members were interviewed about malaria risk factors and their perceived level of mosquito nuisance. RESULTS: Tiled roofs, walls made of mud and wood, as well as the number of occupants in the house were predictors for the number of mosquitoes (Culicidae) in the houses, while the presence of eaves plus walls made of mud and wood were predictors for malaria vector abundance. Perception of mosquito nuisance reported indoors tended to be significantly correlated with the number of Anopheles gambiae sensu lato (s.l.) and Culicidae collected indoors, but this varied across years and sectors. At the village level, nuisance also significantly correlated with An. gambiae s.l. and total mosquito density, but only in 2018 while not in 2017. CONCLUSIONS: Perception of mosquito nuisance denoted in a questionnaire survey could be used as a global indicator of malaria vector hotspots. Hence, involving citizens in such activities can complement malaria vector surveillance and control.

Citizen science for monitoring the spatial and temporal dynamics of malaria vectors in relation to environmental risk factors in Ruhuha, Rwanda.

BACKGROUND: As part of malaria prevention and control efforts, the distribution and density of malaria mosquitoes requires continuous monitoring. Resources for long-term surveillance of malaria vectors, however, are often limited. The aim of the research was to evaluate the value of citizen science in providing insight into potential malaria vector hotspots and other malaria relevant information, and to determine predictors of malaria vector abundance in a region where routine mosquito monitoring has not been established to support vector surveillance. METHODS: A 1-year citizen science programme for malaria mosquito surveillance was implemented in five villages of the Ruhuha sector in Bugesera district, Rwanda. In total, 112 volunteer citizens were enrolled and reported monthly data on mosquitoes collected in their peridomestic environment using handmade carbon-dioxide baited traps. Additionally, they reported mosquito nuisance experienced as well as the number of confirmed malaria cases in their household. RESULTS: In total, 3793 female mosquitoes were collected, of which 10.8% were anophelines. For the entire period, 16% of the volunteers reported having at least one confirmed malaria case per month, but this varied by village and month. During the study year 66% of the households reported at least one malaria case. From a sector perspective, a higher mosquito and malaria vector abundance was observed in the two villages in the south of the study area. The findings revealed significant positive correlations among nuisance reported and confirmed malaria cases, and also between total number of Culicidae and confirmed malaria cases, but not between the numbers of the malaria vector Anopheles gambiae and malaria cases. At the sector level, of thirteen geographical risk factors considered for inclusion in multiple regression, distance to the river network and elevation played a role in explaining mosquito and malaria mosquito abundance. CONCLUSIONS: The study demonstrates that a citizen science approach can contribute to mosquito monitoring, and can help to identify areas that, in view of limited resources for control, are at higher risk of malaria.

Use of trained scent dogs for detection of COVID-19 and evidence of cost-saving.

Background: One of the lessons learned from the coronavirus disease 2019 (COVID-19) pandemic is the importance of early, flexible, and rapidly deployable disease detection methods. Currently, diagnosis of COVID-19 requires the collection of oro/nasopharyngal swabs, nasal turbinate, anterior nares and saliva but as the pandemic continues, disease detection methods that can identify infected individuals earlier and more quickly will be crucial for slowing the spread of the virus. Previous studies have indicated that dogs can be trained to identify volatile organic compounds (VOCs) produced during respiratory infections. We sought to determine whether this approach could be applied for detection of COVID-19 in Rwanda and measured its cost-saving. Methods: Over a period of 5 months, four dogs were trained to detect VOCs in sweat samples collected from human subjects confirmed positive or negative for COVID-19 by reverse transcription polymerase chain reaction (RT-PCR) testing. Dogs were trained using a detection dog training system (DDTS) and in vivo diagnosis. Samples were collected from 5,253 participants using a cotton pad swiped in the underarm to collect sweat samples. Statistical analysis was conducted using R statistical software. Findings: From August to September 2021 during the Delta wave, the sensitivity of the dogs' COVID-19 detection ranged from 75.0 to 89.9% for the lowest- and highest-performing dogs, respectively. Specificity ranged from 96.1 to 98.4%, respectively. In the second phase coinciding with the Omicron wave (January-March 2022), the sensitivity decreased substantially from 36.6 to 41.5%, while specificity remained above 95% for all four dogs. The sensitivity and specificity by any positive sample detected by at least one dog was 83.9, 95% CI: 75.8-90.2 and 94.9%; 95% CI: 93.9-95.8, respectively. The use of scent detection dogs was also found to be cost-saving compared to antigen rapid diagnostic tests, based on a marginal cost of approximately $14,000 USD for testing of the 5,253 samples which makes 2.67 USD per sample. Testing turnaround time was also faster with the scent detection dogs, at 3 h compared to 11 h with routine diagnostic testing. Conclusion: The findings from this study indicate that trained dogs can accurately identify respiratory secretion samples from asymptomatic and symptomatic COVID-19 patients timely and cost-effectively. Our findings recommend further uptake of this approach for COVID-19 detection.