OBJECTIVE: The main objective of this study is to consolidate and analyse the dengue case dataset amassed by the e-Dengue web-based information system, developed by the Ministry of Health Malaysia, to improve our epidemiological understanding.
METHODS: We retrieved data from the e-Dengue system and integrated a total of 18,812 cases from 2012 to 2019 (8 years) with meteorological data, geoinformatics techniques, and socio-environmental observations to identify plausible factors that could have caused dengue outbreaks in Ipoh, a hyperendemic city in Malaysia.
RESULTS: The rainfall trend characterised by a linearity of R2 > 0.99, termed the "wet-dry steps", may be the unifying factor for triggering dengue outbreaks, though it is still a hypothesis that needs further validation. Successful mapping of the dengue "reservoir" contact zones and spill-over diffusion revealed socio-environmental factors that may be controlled through preventive measures. Age is another factor to consider, as the platelet and white blood cell counts in the "below 5" age group are much greater than in other age groups.
CONCLUSIONS: Our work demonstrates the novelty of the e-Dengue system, which can identify outbreak factors at high resolution when integrated with non-medical fields. Besides dengue, the techniques and insights laid out in this paper are valuable, at large, for advancing control strategies for other mosquito-borne diseases such as malaria, chikungunya, and zika in other hyperendemic cities elsewhere globally.
OBJECTIVE: This study aimed to investigate the associations between meteorological factors and the daily number of new cases of COVID-19 in 9 Asian cities.
METHODS: Pearson correlation and generalized additive modeling (GAM) were performed to assess the relationships between daily new COVID-19 cases and meteorological factors (daily average temperature and relative humidity) with the most updated data currently available.
RESULTS: The Pearson correlation showed that daily new confirmed cases of COVID-19 were more correlated with the average temperature than with relative humidity. Daily new confirmed cases were negatively correlated with the average temperature in Beijing (r=-0.565, P
METHOD: A generalized linear model (GLM) estimates the relationships between different travel mode indicators (e.g., length of motorway per inhabitants, number of motorcycles per inhabitant, percentage of daily trips on foot and by bicycle, percentage of daily trips by public transport) and the number of passenger transport fatalities. Because this city-level model is developed using data sets from different cities all over the world, the impacts of gross domestic product (GDP) are also included in the model.
CONCLUSIONS: Overall, the results imply that the percentage of daily trips by public transport, the percentage of daily trips on foot and by bicycle, and the GDP per inhabitant have negative relationships with the number of passenger transport fatalities, whereas motorway length and the number of motorcycles have positive relationships with the number of passenger transport fatalities.
METHODS: Five graph models were fit using data from 1574 people who inject drugs in Hartford, CT, USA. We used a degree-corrected stochastic block model, based on goodness-of-fit, to model networks of injection drug users. We simulated transmission of HCV and HIV through this network with varying levels of HCV treatment coverage (0%, 3%, 6%, 12%, or 24%) and varying baseline HCV prevalence in people who inject drugs (30%, 60%, 75%, or 85%). We compared the effectiveness of seven treatment-as-prevention strategies on reducing HCV prevalence over 10 years and 20 years versus no treatment. The strategies consisted of treatment assigned to either a randomly chosen individual who injects drugs or to an individual with the highest number of injection partners. Additional strategies explored the effects of treating either none, half, or all of the injection partners of the selected individual, as well as a strategy based on respondent-driven recruitment into treatment.
FINDINGS: Our model estimates show that at the highest baseline HCV prevalence in people who inject drugs (85%), expansion of treatment coverage does not substantially reduce HCV prevalence for any treatment-as-prevention strategy. However, when baseline HCV prevalence is 60% or lower, treating more than 120 (12%) individuals per 1000 people who inject drugs per year would probably eliminate HCV within 10 years. On average, assigning treatment randomly to individuals who inject drugs is better than targeting individuals with the most injection partners. Treatment-as-prevention strategies that treat additional network members are among the best performing strategies and can enhance less effective strategies that target the degree (ie, the highest number of injection partners) within the network.
INTERPRETATION: Successful HCV treatment as prevention should incorporate the baseline HCV prevalence and will achieve the greatest benefit when coverage is sufficiently expanded.
FUNDING: National Institute on Drug Abuse.