On the impact of residential history in the spatial analysis of diseases with a long latency period: A study of mesothelioma in Belgium

Mesothelioma is a rare cancer caused by exposure to asbestos. Belgium has a known long history of asbestos production, resulting in one of the highest mesothelioma mortality rates worldwide. While the production of asbestos has stopped completely, the long latency period of mesothelioma, which can fluctuate between 20 and 40 years after exposure, causes incidences still to be frequent. Mesothelioma's long incubation time affects our assessment of its geographical distribution as well. Since patients' residential locations are likely to change a number of times throughout their lives, the location where the patients develop the disease is often far from the location where they were exposed to asbestos. Using the residential history of patients, we propose the use of a convolution multiple membership model (MMM), which includes both a spatial conditional autoregressive and an unstructured random effect. Pancreatic cancer patients are used as a control population, reflecting the population at risk for mesothelioma. Results show the impact of the residential mobility on the geographical risk estimation, as well as the importance of acknowledging the latency period of a disease. A simulation study was conducted to investigate the properties of the convolution MMM. The robustness of the results for the convolution MMM is assessed via a sensitivity analysis.status: publishe

Winter agri-environment schemes and local landscape composition influence the distribution of wintering farmland birds

Since 1992, the European Union puts in place agri-environment schemes (AES), such as unharvested set-aside fields with winter bird crops (WBC), to counteract farmland biodiversity declines that are associated with agricultural intensification since the second half of the 20th century. These measures aim at, among other things, improving habitat quality and food availability for farmland birds throughout the year. In this study in Dry Hesbaye, an agricultural region in eastern Flanders (Belgium), we use spatial generalized linear mixed models to investigate how species richness and the observation probability of ten bird species with different food diets are associated during winter (November - March) with WBC implementation in arable crop fields and the presence of landscape elements within 50 m of these fields. Our results show that species richness and the observation probabilities of nine out of ten wintering farmland bird species under study are increased at crop fields with WBC implementation. Species richness and observation probabilities are also associated with the presence of nearby landscape elements such as hedgerows, woodland, unpaved roads, or grass margins. We conclude that unharvested set-aside fields promote local diversity and observation probabilities of most of the species under study. In addition, AES measures should be implemented after considering the aforementioned natural or semi-natural nearby landscape elements, which also influence local diversity and species’ observation probability

A spatial model to jointly analyze self-reported survey data of COVID-19 symptoms and official COVID-19 incidence data

This work presents a joint spatial modeling framework to improve estimation of the spatial distribution of the latent COVID‐19 incidence in Belgium, based on test‐confirmed COVID‐19 cases and crowd‐sourced symptoms data as reported in a large‐scale online survey. Correction is envisioned for stochastic dependence between the survey's response rate and spatial COVID‐19 incidence, commonly known as preferential sampling, but not found significant. Results show that an online survey can provide valuable auxiliary data to optimize spatial COVID‐19 incidence estimation based on confirmed cases in situations with limited testing capacity. Furthermore, it is shown that an online survey on COVID‐19 symptoms with a sufficiently large sample size per spatial entity is capable of pinpointing the same locations that appear as test‐confirmed clusters, approximately 1 week earlier. We conclude that a large‐scale online study provides an inexpensive and flexible method to collect timely information of an epidemic during its early phase, which can be used by policy makers in an early phase of an epidemic and in conjunction with other monitoring systems

A data-driven metapopulation model for the Belgian COVID-19 epidemic : assessing the impact of lockdown and exit strategies

BACKGROUND: In response to the ongoing COVID-19 pandemic, several countries adopted measures of social distancing to a different degree. For many countries, after successfully curbing the initial wave, lockdown measures were gradually lifted. In Belgium, such relief started on May 4th with phase 1, followed by several subsequent phases over the next few weeks. METHODS: We analysed the expected impact of relaxing stringent lockdown measures taken according to the phased Belgian exit strategy. We developed a stochastic, data-informed, meta-population model that accounts for mixing and mobility of the age-structured population of Belgium. The model is calibrated to daily hospitalization data and is able to reproduce the outbreak at the national level. We consider different scenarios for relieving the lockdown, quantified in terms of relative reductions in pre-pandemic social mixing and mobility. We validate our assumptions by making comparisons with social contact data collected during and after the lockdown. RESULTS: Our model is able to successfully describe the initial wave of COVID-19 in Belgium and identifies interactions during leisure/other activities as pivotal in the exit strategy. Indeed, we find a smaller impact of school re-openings as compared to restarting leisure activities and re-openings of work places. We also assess the impact of case isolation of new (suspected) infections, and find that it allows re-establishing relatively more social interactions while still ensuring epidemic control. Scenarios predicting a second wave of hospitalizations were not observed, suggesting that the per-contact probability of infection has changed with respect to the pre-lockdown period. CONCLUSIONS: Contacts during leisure activities are found to be most influential, followed by professional contacts and school contacts, respectively, for an impending second wave of COVID-19. Regular re-assessment of social contacts in the population is therefore crucial to adjust to evolving behavioral changes that can affect epidemic diffusion. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s12879-021-06092-w)

Modelling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories

Following the onset of the ongoing COVID-19 pandemic throughout the world, a large fraction of the global population is or has been under strict measures of physical distancing and quarantine, with many countries being in partial or full lockdown. These measures are imposed in order to reduce the spread of the disease and to lift the pressure on healthcare systems. Estimating the impact of such interventions as well as monitoring the gradual relaxing of these stringent measures is quintessential to understand how resurgence of the COVID-19 epidemic can be controlled for in the future. In this paper we use a stochastic age-structured discrete time compartmental model to describe the transmission of COVID-19 in Belgium. Our model explicitly accounts for age-structure by integrating data on social contacts to (i) assess the impact of the lockdown as implemented on March 13, 2020 on the number of new hospitalizations in Belgium; (ii) conduct a scenario analysis estimating the impact of possible exit strategies on potential future COVID-19 waves. More specifically, the aforementioned model is fitted to hospital admission data, data on the daily number of COVID-19 deaths and serial serological survey data informing the (sero)prevalence of the disease in the population while relying on a Bayesian MCMC approach. Our age-structured stochastic model describes the observed outbreak data well, both in terms of hospitalizations as well as COVID-19 related deaths in the Belgian population. Despite an extensive exploration of various projections for the future course of the epidemic, based on the impact of adherence to measures of physical distancing and a potential increase in contacts as a result of the relaxation of the stringent lockdown measures, a lot of uncertainty remains about the evolution of the epidemic in the next months