Mechanistic models for West Nile virus transmission: a systematic review of features, aims and parametrization

Mathematical models within the Ross-Macdonald framework increasingly play a role in our understanding of vector-borne disease dynamics and as tools for assessing scenarios to respond to emerging threats. These threats are typically characterized by a high degree of heterogeneity, introducing a range of possible complexities in models and challenges to maintain the link with empirical evidence. We systematically identified and analysed a total of 77 published papers presenting compartmental West Nile virus (WNV) models that use parameter values derived from empirical studies. Using a set of 15 criteria, we measured the dissimilarity compared with the Ross-Macdonald framework. We also retrieved the purpose and type of models and traced the empirical sources of their parameters. Our review highlights the increasing refinements in WNV models. Models for prediction included the highest number of refinements. We found uneven distributions of refinements and of evidence for parameter values. We identified several challenges in parametrizing such increasingly complex models. For parameters common to most models, we also synthesize the empirical evidence for their values and ranges. The study highlights the potential to improve the quality of WNV models and their applicability for policy by establishing closer collaboration between mathematical modelling and empirical work

Supplementary Material 3: from Mechanistic models for West Nile virus transmission: a systematic review of features, aims and parametrization

Classificatio

Supplementary Material 6: from Mechanistic models for West Nile virus transmission: a systematic review of features, aims and parametrization

Variation in parameters estimates based on the most cited empirical stud

The potential of resilience indicators to anticipate infectious disease outbreaks, a systematic review and guide.

To reduce the consequences of infectious disease outbreaks, the timely implementation of public health measures is crucial. Currently used early-warning systems are highly context-dependent and require a long phase of model building. A proposed solution to anticipate the onset or termination of an outbreak is the use of so-called resilience indicators. These indicators are based on the generic theory of critical slowing down and require only incidence time series. Here we assess the potential for this approach to contribute to outbreak anticipation. We systematically reviewed studies that used resilience indicators to predict outbreaks or terminations of epidemics. We identified 37 studies meeting the inclusion criteria: 21 using simulated data and 16 real-world data. 36 out of 37 studies detected significant signs of critical slowing down before a critical transition (i.e., the onset or end of an outbreak), with a highly variable sensitivity (i.e., the proportion of true positive outbreak warnings) ranging from 0.03 to 1 and a lead time ranging from 10 days to 68 months. Challenges include low resolution and limited length of time series, a too rapid increase in cases, and strong seasonal patterns which may hamper the sensitivity of resilience indicators. Alternative types of data, such as Google searches or social media data, have the potential to improve predictions in some cases. Resilience indicators may be useful when the risk of disease outbreaks is changing gradually. This may happen, for instance, when pathogens become increasingly adapted to an environment or evolve gradually to escape immunity. High-resolution monitoring is needed to reach sufficient sensitivity. If those conditions are met, resilience indicators could help improve the current practice of prediction, facilitating timely outbreak response. We provide a step-by-step guide on the use of resilience indicators in infectious disease epidemiology, and guidance on the relevant situations to use this approach

Supplementary Material 1: from Mechanistic models for West Nile virus transmission: a systematic review of features, aims and parametrization

Systematic database search routin

Supplementary Material 4: from Mechanistic models for West Nile virus transmission: a systematic review of features, aims and parametrization

Parameter

Supplementary Material 5: from Mechanistic models for West Nile virus transmission: a systematic review of features, aims and parametrization

Original Source

Supplementary Material 2: from Mechanistic models for West Nile virus transmission: a systematic review of features, aims and parametrization

Paper selectio

Overview of the 37 papers included in this review.

A Number of included papers per year. The number of studies on resilience indicators to anticipate epidemics has shown an increasing trend in the last few years. Since 2020, more studies have been published as data on the COVID-19 pandemic became publicly available. B Included papers classified according to the type of study into three categories: case studies, simulation studies, and simulation studies supported by case studies. C Included papers classified according to the type of transition, into three categories: the onset of an outbreak, disease elimination, and both.</p

Decision tree.

Step-by-step approach to use resilience indicators in epidemiology.</p