Amebiasis, which is caused by Entamoeba histolytica (E. histolytica), is the second leading cause of parasite-related death worldwide. It manifests from asymptomatic carriers to severe clinical conditions, like colitis and liver abscesses. Amebiasis is commonly seen in developing countries, where water and food are easily contaminated by feces because of the poor sanitation. However, a recently challenge in many developed countries is the increase in domestic cases of invasive amebiasis as a sexually transmitted infection (STI amebiasis). In contrast to food-/ waterborne transmission of E. histolytica in developing countries, transmission of STI amebiasis occurs directly through human-to-human sexual contact (e.g., men who have sex with men and people who engage in oral-anal sex); in this setting, asymptomatic infected individuals are the main reservoir of E. histolytica. The Development of screening methods for the early diagnosis of asymptomatic E. histolytica infection is the key to epidemiologic control. Moreover, delay in diagnosis of severe cases (e.g., fulminant amebiasis) leads to death even in developed countries. It is also important to increase clinical awareness of domestically transmitted STI amebiasis in the clinical settings. This review considers the changing epidemiology and clinical manifestations of STI amebiasis, and finally discusses the future strategies for the better practice.

Disease control programs are needed to identify the breeding sites of mosquitoes, which transmit malaria and other diseases, in order to target interventions and identify environmental risk factors. The increasing availability of very-high-resolution drone data provides new opportunities to find and characterize these vector breeding sites. Within this study, drone images from two malaria-endemic regions in Burkina Faso and Côte d\'Ivoire were assembled and labeled using open-source tools. We developed and applied a workflow using region-of-interest-based and deep learning methods to identify land cover types associated with vector breeding sites from very-high-resolution natural color imagery. Analysis methods were assessed using cross-validation and achieved maximum Dice coefficients of 0.68 and 0.75 for vegetated and non-vegetated water bodies, respectively. This classifier consistently identified the presence of other land cover types associated with the breeding sites, obtaining Dice coefficients of 0.88 for tillage and crops, 0.87 for buildings and 0.71 for roads. This study establishes a framework for developing deep learning approaches to identify vector breeding sites and highlights the need to evaluate how results will be used by control programs.

In Brazil, the mosquito Aedes (Stegomyia) aegypti is considered the main vector of the dengue, chikungunya, and Zika arbovirus transmission. Recent epidemiological studies in southern Brazil have shown an increase in the incidence of dengue, raising concerns over epidemiological control, monitoring, and surveys. Therefore, this study aimed at performing a historical spatiotemporal analysis of the Ae. aegypti house indices (HI) in southern Brazil over the last 19 years. As vector infestation was associated with climatic and environmental variables, HI data from the Brazilian Ministry of Health, climate data from the Giovanni web-based application, and environmental data from the Mapbiomas project were used in this study. Our results showed an expressive increase in the number of HI surveys in the municipalities confirming the vector presence, as compared to those in 2017. Environmental variables, such as urban infrastructure, precipitation, temperature, and humidity, were positively correlated with the Ae. aegypti HI. This was the first study to analyze Ae. aegypti HI surveys in municipalities of southern Brazil, and our findings could help in developing and planning disease control strategies to improve public health.

BACKGROUND: For many emerging or re-emerging pathogens, cases in humans arise from a mixture of introductions (via zoonotic spillover from animal reservoirs or geographic spillover from endemic regions) and secondary human-to-human transmission. Interventions aiming to reduce incidence of these infections can be focused on preventing spillover or reducing human-to-human transmission, or sometimes both at once, and typically are governed by resource constraints that require policymakers to make choices. Despite increasing emphasis on using mathematical models to inform disease control policies, little attention has been paid to guiding rational disease control at the animal-human interface.
METHODS: We introduce a modeling framework to analyze the impacts of different disease control policies, focusing on pathogens exhibiting subcritical transmission among humans (i.e. pathogens that cannot establish sustained human-to-human transmission). We quantify the relative effectiveness of measures to reduce spillover (e.g. reducing contact with animal hosts), human-to-human transmission (e.g. case isolation), or both at once (e.g. vaccination), across a range of epidemiological contexts.
RESULTS: We provide guidelines for choosing which mode of control to prioritize in different epidemiological scenarios and considering different levels of resource and relative costs. We contextualize our analysis with current zoonotic pathogens and other subcritical pathogens, such as post-elimination measles, and control policies that have been applied.
CONCLUSIONS: Our work provides a model-based, theoretical foundation to understand and guide policy for subcritical zoonoses, integrating across disciplinary and species boundaries in a manner consistent with One Health principles.

Personal protection measures, such as bed nets and repellents, are important tools for the suppression of vector-borne diseases like malaria and Zika, and the ability of health agencies to distribute protection and encourage its use plays an important role in the efficacy of community-wide disease management strategies. Recent modelling studies have shown that a counterintuitive diversity-driven amplification in community-wide disease levels can result from a population\'s partial adoption of personal protection measures, potentially to the detriment of disease management efforts. This finding, however, may overestimate the negative impact of partial personal protection as a result of implicit restrictive model assumptions regarding host compliance, access to and longevity of protection measures. We establish a new modelling methodology for incorporating community-wide personal protection distribution programmes in vector-borne disease systems which flexibly accounts for compliance, access, longevity and control strategies by way of a flow between protected and unprotected populations. Our methodology yields large reductions in the severity and occurrence of amplification effects as compared to existing models.

A multitude of resistance deployment strategies have been proposed to tackle the evolutionary potential of pathogens to overcome plant resistance. In particular, many landscape-based strategies rely on the deployment of resistant and susceptible cultivars in an agricultural landscape as a mosaic. However, the design of such strategies is not easy as strategies targeting epidemiological or evolutionary outcomes may not be the same. Using a stochastic spatially explicit model, we studied the impact of landscape organization (as defined by the proportion of fields cultivated with a resistant cultivar and their spatial aggregation) and key pathogen life-history traits on three measures of disease control. Our results show that short-term epidemiological dynamics are optimized when landscapes are planted with a high proportion of the resistant cultivar in low aggregation. Importantly, the exact opposite situation is optimal for resistance durability. Finally, well-mixed landscapes (balanced proportions with low aggregation) are optimal for long-term evolutionary equilibrium (defined here as the level of long-term pathogen adaptation). This work offers a perspective on the potential for contrasting effects of landscape organization on different goals of disease management and highlights the role of pathogen life history.