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.

Assessing West Nile virus (WNV) and Usutu virus (USUV) exposure in bird ringers in the Netherlands: a high-risk group for WNV and USUV infection?

Introduction

In 2020, the first Dutch West Nile virus (WNV) infected birds were detected through risk-targeted surveillance of songbirds. Retrospective testing of patients with unexplained neurological disease revealed human WNV infections in July and August 2020. Bird ringers are highly exposed to mosquito bites and possibly avian excrements during ringing activities. This study therefore investigates whether bird ringers are at higher risk of exposure to WNV and Usutu virus (USUV).

Methods

Dutch bird ringers were asked to provide a single serum sample (May - September 2021) and to fill out a survey. Sera were screened by protein microarray for presence of specific IgG against WNV and USUV non-structural protein 1 (NS1), followed by focus reduction virus neutralization tests (FRNT). Healthcare workers (2009-2010), the national immunity cohort (2016-2017) and blood donors (2021) were used as control groups without this occupational exposure.

Results

The majority of the 157 participating bird ringers was male (132/157, 84%) and the median age was 62 years. Thirty-seven participants (37/157, 23.6%) showed WNV and USUV IgG microarray signals above background, compared to 6.4% (6/94) in the community cohort and 2.1% (2/96) in blood donors (p < 0.01). Two seroreactive bird ringers were confirmed WNV or USUV positive by FRNT. The majority of seroreactive bird ringers travelled to EU countries with reported WNV human cases (30/37, 81%) (p = 0.07). No difference was observed between bird ringers with and without previous yellow fever vaccination.

Discussion

The higher frequency of WNV and/or USUV IgG reactive bird ringers indicates increased flavivirus exposure compared to the general population, suggesting that individuals with high-exposure professions may be considered to complement existing surveillance systems. However, the complexity of serological interpretation in relation to location-specific exposure (including travel), and antibody cross-reactivity, remain a challenge when performing surveillance of emerging flaviviruses in low-prevalence settings.

Exploring the influence of competition on arbovirus invasion risk in communities

Arbovirus outbreaks in communities are affected by how vectors, hosts and non-competent species interact. In this study, we investigate how ecological interactions between species and epidemiological processes influence the invasion potential of a vector-borne disease. We use an eco-epidemiological model to explore the basic reproduction number R0 for a range of interaction strengths in key processes, using West Nile virus infection to parameterize the model. We focus our analysis on intra and interspecific competition between vectors and between hosts, as well as competition with non-competent species. We show that such ecological competition has non-linear effects on R0 and can greatly impact invasion risk. The presence of multiple competing vector species results in lower values for R0 while host competition leads to the highest values of risk of disease invasion. These effects can be understood in terms of how the competitive pressures influence the vector-to-host ratio, which has a positive relationship with R0. We also show numerical examples of how vector feeding preferences become more relevant in high competition conditions between hosts. Under certain conditions, non-competent hosts, which can lead to a dilution effect for the pathogen, can have an amplification effect if they compete strongly with the competent hosts, hence facilitating pathogen invasion in the community.

Analytical framework to evaluate and optimize the use of imperfect diagnostics to inform outbreak response: Application to the 2017 plague epidemic in Madagascar

During outbreaks, the lack of diagnostic “gold standard” can mask the true burden of infection in the population and hamper the allocation of resources required for control. Here, we present an analytical framework to evaluate and optimize the use of diagnostics when multiple yet imperfect diagnostic tests are available. We apply it to laboratory results of 2,136 samples, analyzed with 3 diagnostic tests (based on up to 7 diagnostic outcomes), collected during the 2017 pneumonic (PP) and bubonic plague (BP) outbreak in Madagascar, which was unprecedented both in the number of notified cases, clinical presentation, and spatial distribution. The extent of these outbreaks has however remained unclear due to nonoptimal assays. Using latent class methods, we estimate that 7% to 15% of notified cases were Yersinia pestis-infected. Overreporting was highest during the peak of the outbreak and lowest in the rural settings endemic to Y. pestis. Molecular biology methods offered the best compromise between sensitivity and specificity. The specificity of the rapid diagnostic test was relatively low (PP: 82%, BP: 85%), particularly for use in contexts with large quantities of misclassified cases. Comparison with data from a subsequent seasonal Y. pestis outbreak in 2018 reveal better test performance (BP: specificity 99%, sensitivity: 91%), indicating that factors related to the response to a large, explosive outbreak may well have affected test performance. We used our framework to optimize the case classification and derive consolidated epidemic trends. Our approach may help reduce uncertainties in other outbreaks where diagnostics are imperfect.

The response to the 2017 plague outbreak in Madagascar was complicated by the lack of a perfect or "gold standard" diagnostic. This study shows how multiple, imperfect diagnostic tests can be used to improve the response to an outbreak.