The impact of natural climate variability on the global distribution of Aedes aegypti: a mathematical modelling study.

Background: Aedes aegypti spread pathogens affecting humans, including dengue, Zika, and yellow fever viruses. Anthropogenic climate change is altering the spatial distribution of Ae aegypti and therefore the locations at risk of vector-borne disease. In addition to climate change, natural climate variability, resulting from internal atmospheric processes and interactions between climate system components (eg, atmosphere-land and atmosphere-ocean interactions), determines climate outcomes.

The time between symptom onset and various clinical outcomes: a statistical analysis of MERS-CoV patients in Saudi Arabia.

In this study, we investigate the impact of demographic characteristics on Middle East respiratory syndrome coronavirus (MERS-CoV) cases in Saudi Arabia, specifically focusing on the time intervals between symptom onset and key events such as hospitalization, case confirmation, reporting and death. We estimate these intervals using data from 2196 cases occurring between June 2012 and January 2020, partitioning the data into four age groups (0-24 years, 25-49 years, 50-74 years and 75-100 years). The duration from symptom onset to hospitalization varies between age cohorts, ranging from 4.

Modelling the Influence of Climate and Vector Control Interventions on Arbovirus Transmission.

Most mathematical models that assess the vectorial capacity of disease-transmitting insects typically focus on the influence of climatic factors to predict variations across different times and locations, or examine the impact of vector control interventions to forecast their potential effectiveness. We combine features of existing models to develop a novel model for vectorial capacity that considers both climate and vector control. This model considers how vector control tools affect vectors at each stage of their feeding cycle, and incorporates host availability and preference.