Malaria hotspots have been the focus of public health managers for several years due to the potential elimination gains that can be obtained from targeting them. The identification of hotspots must be accompanied by the description of the overall network of stable and unstable hotspots of malaria, especially in medium and low transmission settings where malaria elimination is targeted. Targeting hotspots with malaria control interventions has, so far, not produced expected benefits. In this work we have employed a mechanistic-stochastic algorithm to identify clusters of super-spreader houses and their related stable hotspots by accounting for mosquito flight capabilities and the spatial configuration of malaria infections at the house level. Our results show that the number of super-spreading houses and hotspots is dependent on the spatial configuration of the villages. In addition, super-spreaders are also associated to house characteristics such as livestock and family composition. We found that most of the transmission is associated with winds between 6pm and 10pm although later hours are also important. Mixed mosquito flight (downwind and upwind both with random components) were the most likely movements causing the spread of malaria in two out of the three study areas. Finally, our algorithm (named MALSWOTS) provided an estimate of the speed of malaria infection progression from house to house which was around 200–400 meters per day, a figure coherent with mark-release-recapture studies of Anopheles dispersion. Cross validation using an out-of-sample procedure showed accurate identification of hotspots. Our findings provide a significant contribution towards the identification and development of optimal tools for efficient and effective spatio-temporal targeted malaria interventions over potential hotspot areas.
BackgroundPublic health interventions are essential to prevent a long tail of costly, avoidable and worsening ill health in coastal communities following the COVID-19 pandemic, yet no research exists to guide policy and practice as to which groups within coastal communities are vulnerable and most in need of such interventions. Within this aim, we explore engrained and emerging vulnerabilities of food insecurity, health and well-being for different demographic groups within the deprived coastal community of Fleetwood, Lancashire, UK, before and after the pandemic.MethodsRoutinely collected data of free school meal eligibility, community mental health referrals and hospital admissions between 28 March 2016 and 31 December 2021 were aggregated by locality and deprivation within Fleetwood. Temporal autoregressive models, generalised linear mixed models and survival analyses were employed to compare trends and associations in food insecurity, health and well-being indicators against deprivation indices, demographics, comorbidities (including COVID-19), the COVID-19 pandemic period and locality.ResultsAreas with better housing and income, but higher health and disability deprivation, showed increased levels of free school meal eligibility following the pandemic. Mental health was insensitive to the first 14 months of pandemic yet is worsened by unemployment deprivation and cardiovascular and respiratory comorbidities, with a greater predisposition to poor mental health in adolescents and young adults. After accounting for the effect of COVID-19, hospital mortality risk increased with demographic influences in fitting with the typology of coastal communities having an older population, struggling healthcare and a greater prevalence of comorbidities.ConclusionsPublic health managers and policy makers seeking to prevent worsening health and well-being within coastal communities following the pandemic should focus on broader-scale patterns reflecting entrenched poor health typical of coastal communities, and emerging food insecurity within specific demographic and deprivation groups at finer scales.
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