Socioeconomic and environmental determinants of foot and mouth disease incidence: an ecological, cross-sectional study across Iran using spatial modeling.

Foot-and-mouth disease (FMD) is a highly contagious animal disease caused by a ribonucleic acid (RNA) virus, with significant economic costs and uneven distribution across Asia, Africa, and South America. While spatial analysis and modeling of FMD are still in their early stages, this research aimed to identify socio-environmental determinants of FMD incidence in Iran at the provincial level by studying 135 outbreaks reported between March 21, 2017, and March 21, 2018. We obtained 46 potential socio-environmental determinants and selected four variables, including percentage of population, precipitation in January, percentage of sheep, and percentage of goats, to be used in spatial regression models to estimate variation in spatial heterogeneity. In our analysis, we employed global models, namely ordinary least squares (OLS), spatial error model (SEM), and spatial lag model (SLM), as well as local models, including geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR). The MGWR model yielded the highest adjusted [Formula: see text] of 90%, outperforming the other local and global models. Using local models to map the effects of environmental determinants (such as the percentage of sheep and precipitation) on the spatial variability of FMD incidence provides decision-makers with helpful information for targeted interventions. Our findings advocate for multiscale and multidisciplinary policies to reduce FMD incidence.

Environmental and infrastructural effects on respiratory disease exacerbation: a LBSN and ANN-based spatio-temporal modelling.

Owing to the rise in population, lifestyle changes, high traffic rates in urban areas and environmental pollution, respiratory diseases have become much more prevalent on both regional and urban scales. Respiratory diseases affect over 300 million people worldwide and are thus among the major threats to humans' general well-being. The identification of underlying factors and the specification of accompanying risk areas for the temporal exacerbation of respiratory diseases are effective steps in managing the damage caused by such disorders. Here, we demonstrate a strategy for modelling the risk zone of respiratory diseases temporally, using a location-based social network (LBSN) and an artificial neural network (ANN). The main contribution of this paper is to consider the environmental and infrastructural factors and identify their relationships with the geographical locations of respiratory attacks. The study also utilizes Telegram, which is the most popular and conventional social media platform, in order to observe temporal changes in the location of respiratory attacks in Iran, in the form of a developed Telegram bot known as @respiratoryassociation. The relations between the factors behind and the location of respiratory attacks are determined using a multilayer perceptron (MLP) ANN. All the required data have been collected on a daily basis over a 5-year period from December 2013 to December 2018 in Tehran, Iran. The results indicated air pollution, especially pollution from carbon monoxide (CO) and suspended particulate matter (PM) as the most decisive factors. Following air pollution, the amount of exposure to the polluted area was determined as the second most decisive factor, which in turn increased as a result of escalations in traffic jams. Land use was determined as the third most decisive factor. Furthermore, the results revealed that the ANN performed satisfactorily, implying that the model can be used to examine the spatio-temporal behaviour of the time series of respiratory diseases with respect to environmental and infrastructural factors.