Applications of artificial intelligence in predicting dengue outbreaks in the face of climate change: a case study along coastal India.
Tallam, K.; Quang, M. P.
Show abstract
The climate crisis will have an increasingly profound effect on the global distribution and burden of infectious diseases. Climate-sensitive diseases can serve as critical case studies for assessing public health priorities in the face of epidemics. Preliminary results denote that machine learning-based predictive modeling measures can be successfully applied to understanding environmental disease transmission dynamics. Ultimately, machine learning models can be trained to detect climate-sensitive diseases early, diseases which might represent serious threats to human health, food safety, and economies. We explore how machine learning can serve as a tool for better understanding climate-sensitive diseases, taking dengue dynamics along the Godavari River of coastal India as our case study. We hypothesize that a climate-driven predictive model with controlled calibration can help us understand several of the most critical relationships and climate characteristics of climate-sensitive disease dynamics.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Time series models for prediction of Leptospirosis in different climate zones in Sri LankaTime series models for prediction of Leptospirosis in different climate zones in Sri Lanka 96%
- Interdependence between confirmed and discarded cases of dengue, chikungunya and Zika viruses in Brazil: A multivariate time-series analysis 95%
- Assessing generalizability of a dengue classifier across multiple datasets 95%
Similar papers in this journal
- Global patterns of aegyptism without arbovirus 95%
- Severity Index for Suspected Arbovirus (SISA): machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection 94%
- Predicting Dengue Incidence Leveraging Internet-Based Data Sources. A Case Study in 20 cities in Brazil 94%
Similar papers in this journal
- Determining the effects of preseasonal climate factors toward dengue early warning system in Bangladesh 94%
- Advancing Early Warning Systems for Malaria: Progress, Challenges, and Future Directions - A Scoping Review 94%
- Epidemiological and virological factors determining dengue transmission in Sri Lanka during the COVID-19 pandemic 93%
Similar papers in this journal
Similar papers in this journal
- Modeling of leptospirosis outbreaks in relation to hydroclimatic variables in the northeast of Argentina 95%
- The Wood equation allows consistent fitting of individual antibody responses profiles in Zika virus or SARS-CoV-2 infected patients 92%
- Power spectra density and similarity analysis of COVID-19 mortality waves across countries 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.