Comparing the Performance of Three Models Incorporating Weather Data to Forecast Dengue Epidemics in Reunion Island, 2018-2019

Modeling the impact of vaccine campaigns on the epidemic transmission dynamics of chikungunya virus outbreaks

A licensed chikungunya vaccine now exists; however, it remains unclear whether it could be deployed during outbreaks to reduce the health burden. We used an epidemic in Paraguay as a case study. We conducted a seroprevalence study and used models to reconstruct epidemic transmission dynamics, providing a framework to assess the theoretical impact of a vaccine had it been available. We estimated that 33.0% (95% confidence interval (CI) 30.1-36.0%) of the population became infected during the outbreak. Of these individuals, 6.3% (95% CI 5.8-6.9%) were detected by the surveillance system, with a mean infection fatality ratio of 0.013% (95% CI 0.012-0.014%). A disease-blocking vaccine with 75% efficacy deployed in 40% of individuals aged ≥12 years over a 3-month period would have prevented 34,200 (95% CI 30,900-38,000) cases, representing 23% of all cases, and 73 (95% CI 66-81) deaths. If the vaccine also leads to infection blocking, 88% of cases would have been averted. These findings suggest that the vaccine is an important new tool to control outbreaks.

Why the growth of arboviral diseases necessitates a new generation of global risk maps and future projections

Global risk maps are an important tool for assessing the global threat of mosquito and tick-transmitted arboviral diseases. Public health officials increasingly rely on risk maps to understand the drivers of transmission, forecast spread, identify gaps in surveillance, estimate disease burden, and target and evaluate the impact of interventions. Here, we describe how current approaches to mapping arboviral diseases have become unnecessarily siloed, ignoring the strengths and weaknesses of different data types and methods. This places limits on data and model output comparability, uncertainty estimation and generalisation that limit the answers they can provide to some of the most pressing questions in arbovirus control. We argue for a new generation of risk mapping models that jointly infer risk from multiple data types. We outline how this can be achieved conceptually and show how this new framework creates opportunities to better integrate epidemiological understanding and uncertainty quantification. We advocate for more co-development of risk maps among modellers and end-users to better enable risk maps to inform public health decisions. Prospective validation of risk maps for specific applications can inform further targeted data collection and subsequent model refinement in an iterative manner. If the expanding use of arbovirus risk maps for control is to continue, methods must develop and adapt to changing questions, interventions and data availability.

Assessing the impact of vaccination and medical resource allocation on infectious disease outbreak management: a case study of COVID-19 in Taiyuan City

Introduction

Amidst an emerging infectious disease outbreak, the rational allocation of vaccines and medical resources is crucial for controlling the epidemic's progression.

Method

Analysing COVID-19 data in Taiyuan City from December 2022 to January 2023, this study constructed a S V 1 V 2 V 3 E I Q H R dynamics model to assess the impact of COVID-19 vaccination and resource allocation on epidemic trends.

Results

Vaccination significantly reduces infection rates, hospitalisations, and severe cases, while also curtailing strain on medical resources by reducing congestion periods. An early and sufficient reserve of medical resources can delay the onset of medical congestion, and with increased maximum capacity of medical resources, the congestion's end can be accelerated. Stronger resource allocation capabilities lead to earlier congestion resolution within a fixed total resource pool.

Discussion

Integrating vaccination and medical resource allocation can effectively reduce medical congestion duration and alleviate the epidemic's strain on medical resource capacity (CCMR).

Modeling zoonotic and vector-borne viruses

The 2013-2016 Ebola virus disease epidemic and the coronavirus disease 2019 pandemic galvanized tremendous growth in models for emerging zoonotic and vector-borne viruses. Therefore, we have reviewed the main goals and methods of models to guide scientists and decision-makers. The elements of models for emerging viruses vary across spectrums: from understanding the past to forecasting the future, using data across space and time, and using statistical versus mechanistic methods. Hybrid/ensemble models and artificial intelligence offer new opportunities for modeling. Despite this progress, challenges remain in translating models into actionable decisions, particularly in areas at highest risk for viral disease outbreaks. To address this issue, we must identify gaps in models for specific viruses, strengthen validation, and involve policymakers in model development.