The ongoing Ebola outbreak poses an alarming risk to the countries of West Africa and beyond. To assess the effectiveness of containment strategies, we developed a stochastic model of Ebola transmission between and within the general community, hospitals, and funerals, calibrated to incidence data from Liberia. We find that a combined approach of case isolation, contact-tracing with quarantine, and sanitary funeral practices must be implemented with utmost urgency in order to reverse the growth of the outbreak. As of 19 September, under status quo, our model predicts that the epidemic will continue to spread, generating a predicted 224 (134 to 358) daily cases by 1 December, 280 (184 to 441) by 15 December, and 348 (249 to 545) by 30 December.

For COVID-19, it is vital to understand if quarantines shorter than 14 days can be equally effective with judiciously deployed testing. Here, we develop a mathematical model that quantifies the probability of post-quarantine transmission incorporating testing into travel quarantine, quarantine of traced contacts with an unknown time of infection, and quarantine of cases with a known time of exposure. We find that testing on exit (or entry and exit) can reduce the duration of a 14-day quarantine by 50%, while testing on entry shortens quarantine by at most one day. In a real-world test of our theory applied to offshore oil rig employees, 47 positives were obtained with testing on entry and exit to quarantine, of which 16 had tested negative at entry; preventing an expected nine offshore transmission events that each could have led to outbreaks. We show that appropriately timed testing can make shorter quarantines effective.

Integrating prior epidemiological knowledge embedded within mechanistic models with the data-mining capabilities of artificial intelligence (AI) offers transformative potential for epidemiological modeling. While the fusion of AI and traditional mechanistic approaches is rapidly advancing, efforts remain fragmented. This scoping review provides a comprehensive overview of emerging integrated models applied across the spectrum of infectious diseases. Through systematic search strategies, we identified 245 eligible studies from 15,460 records. Our review highlights the practical value of integrated models, including advances in disease forecasting, model parameterization, and calibration. However, key research gaps remain. These include the need for better incorporation of realistic decision-making considerations, expanded exploration of diverse datasets, and further investigation into biological and socio-behavioral mechanisms. Addressing these gaps will unlock the synergistic potential of AI and mechanistic modeling to enhance understanding of disease dynamics and support more effective public health planning and response.

Quantitative analysis and mathematical models are useful tools in informing strategies to control or eliminate disease. Currently, there is an urgent need to develop these tools to inform policy to achieve the 2020 goals for neglected tropical diseases (NTDs). In this paper we give an overview of a collection of novel model-based analyses which aim to address key questions on the dynamics of transmission and control of nine NTDs: Chagas disease, visceral leishmaniasis, human African trypanosomiasis, leprosy, soil-transmitted helminths, schistosomiasis, lymphatic filariasis, onchocerciasis and trachoma. Several common themes resonate throughout these analyses, including: the importance of epidemiological setting on the success of interventions; targeting groups who are at highest risk of infection or re-infection; and reaching populations who are not accessing interventions and may act as a reservoir for infection,. The results also highlight the challenge of maintaining elimination ‘as a public health problem’ when true elimination is not reached. The models elucidate the factors that may be contributing most to persistence of disease and discuss the requirements for eventually achieving true elimination, if that is possible. Overall this collection presents new analyses to inform current control initiatives. These papers form a base from which further development of the models and more rigorous validation against a variety of datasets can help to give more detailed advice. At the moment, the models’ predictions are being considered as the world prepares for a final push towards control or elimination of neglected tropical diseases by 2020.

Over 20,000 rabies deaths occur annually in India, representing one-third of global human rabies. The Indian state of Tamil Nadu has pioneered a “One Health” committee to address the challenge of rabies in dogs and humans. Currently, rabies control in Tamil Nadu involves postexposure vaccination of humans after dog bites, whereas potential supplemental approaches include canine vaccination and sterilization. We developed a data-driven rabies transmission model fit to human rabies autopsy data and human rabies surveillance data from Tamil Nadu. Integrating local estimates for canine demography and costs, we predicted the impact of canine vaccination and sterilization on human health outcomes and evaluated cost-effectiveness according to the WHO criteria for India, which correspond to thresholds of $1,582 and $4,746 per disability-adjusted life-years (DALYs) for very cost-effective and cost-effective strategies, respectively. We found that highly feasible strategies focused on stray dogs, vaccinating as few as 7% of dogs annually, could very cost-effectively reduce human rabies deaths by 70% within 5 y, and a modest expansion to vaccinating 13% of stray dogs could cost-effectively reduce human rabies by almost 90%. Through integration over parameter uncertainty, we find that, for a cost-effectiveness threshold above $1,400 per DALY, canine interventions are at least 95% likely to be optimal. If owners are willing to bring dogs to central point campaigns at double the rate that campaign teams can capture strays, expanded annual targets become cost-effective. This case study of cost-effective canine interventions in Tamil Nadu may have applicability to other settings in India and beyond.

Significance: Despite extraordinary advances in the treatment of HIV, the global pandemic has yet to be reversed. We developed a mathematical model for 127 countries to evaluate Joint United Nations Program on HIV/AIDS (UNAIDS) targets for expanding diagnosis and treatment of the infected, and partially efficacious HIV vaccination. Under the current levels of diagnosis and treatment, we estimated 49 million new HIV cases globally from 2015 to 2035. Achieving the ambitious UNAIDS target is predicted to avert 25 million of these new infections, with an additional 6.3 million averted by the 2020 introduction of a 50%-efficacy vaccine. Our study provides country-specific impacts of a partially effective HIV vaccine and demonstrates its importance to the elimination of HIV transmission globally.