The mainland clouded leopard Neofelis nebulosa is categorized as Vulnerable on the IUCN Red List and considered at high risk of extinction in the wild. Despite this, knowledge of its ecology and population status remains limited. We investigated the population density, habitat utilization and spatial and temporal ecology of the clouded leopard in Manas National Park, north-east India. We deployed camera traps across the forested habitats of Manas for 11,388 trap-nights, resulting in images of 12 clouded leopard individuals. The estimated population density was 1.73 individuals per 100 km2 and the relative abundance index was 0.18 per 100 trap-nights. The availability of small prey species and primary forests influenced clouded leopard habitat use significantly, highlighting the potential conservation importance of species such as hares, gallinaceous birds, porcupines and primates. We observed a high degree of temporal overlap (> 0.70), with distinct activity peaks, between clouded leopards and sympatric carnivores. We observed no spatial co-occurrence pattern between clouded leopards and sympatric carnivores. This study contributes to our understanding of the mainland clouded leopard population and its behavioural ecology in Manas National Park.

Dengue fever (DF) is one of the world's most disabling mosquito-borne diseases, with a variety of approaches available to model its spatial and temporal dynamics. This paper aims to identify and compare the different spatial and spatio-temporal Bayesian modelling methods that have been applied to DF and examine influential covariates that have been reportedly associated with the risk of DF. A systematic search was performed in December 2017, using Web of Science, Scopus, ScienceDirect, PubMed, ProQuest and Medline (via Ebscohost) electronic databases. The search was restricted to refereed journal articles published in English from January 2000 to November 2017. Thirty-one articles met the inclusion criteria. Using a modified quality assessment tool, the median quality score across studies was 14/16. The most popular Bayesian statistical approach to dengue modelling was a generalised linear mixed model with spatial random effects described by a conditional autoregressive prior. A limited number of studies included spatio-temporal random effects. Temperature and precipitation were shown to often influence the risk of dengue. Developing spatio-temporal random-effect models, considering other priors, using a dataset that covers an extended time period, and investigating other covariates would help to better understand and control DF transmission.

Malaria and leishmaniasis are among the two most important health problems of many developing countries especially in the Middle East and North Africa. It is common for vector-borne infectious diseases to have similar hotspots which may be attributed to the overlapping ecological distribution of the vector. Hotspot analyses were conducted to simultaneously detect the location of local hotspots and test their statistical significance. Spatial scan statistics were used to detect and test hotspots of malaria and cutaneous leishmaniasis (CL) in Afghanistan in 2009. A multivariate negative binomial model was used to simultaneously assess the effects of environmental variables on malaria and CL. In addition to the dependency between malaria and CL disease counts, spatial and temporal information were also incorporated in the model. Results indicated that malaria and CL incidence peaked at the same periods. Two hotspots were detected for malaria and three for CL. The findings in the current study show an association between the incidence of malaria and CL in the studied areas of Afghanistan. The incidence of CL disease in a given month is linked with the incidence of malaria in the previous month. Co-existence of malaria and CL within the same geographical area was supported by this study, highlighting the presence and effects of environmental variables such as temperature and precipitation. People living in areas with malaria are at increased risk for leishmaniasis infection. Local healthcare authorities should consider the co-infection problem by recommending systematic malaria screening for all CL patients.