Modeling the number of hidden events subject to observation delay

This paper considers the problem of predicting the number of events that have occurred in the past, but which are not yet observed due to a delay. Such delayed events are relevant in predicting the future cost of warranties, pricing maintenance contracts, determining the number of unreported claims in insurance and in modeling the outbreak of diseases. Disregarding these unobserved events results in a systematic underestimation of the event occurrence process. Our approach puts emphasis on modeling the time between the occurrence and observation of the event, the so-called observation delay. We propose a granular model for the heterogeneity in this observation delay based on the occurrence day of the event and on calendar day effects in the observation process, such as weekday and holiday effects. We illustrate this approach on a European general liability insurance data set where the occurrence of an accident is reported to the insurer with delay

Modeling the occurrence of events subject to a reporting delay via an EM algorithm

A delay between the occurrence and the reporting of events often has practical implications such as for the amount of capital to hold for insurance companies, or for taking preventive actions in case of infectious diseases. The accurate estimation of the number of incurred but not (yet) reported events forms an essential part of properly dealing with this phenomenon. We review the current practice for analysing such data and we present a flexible regression framework to jointly estimate the occurrence and reporting of events. By linking this setting to an incomplete data problem, estimation is performed by the expectation-maximization algorithm. The resulting method is elegant, easy to understand and implement, and provides refined insights in the nowcasts. The proposed methodology is applied to a European general liability portfolio in insurance

COVID-19 surveillance in the Flemish school system : development of systematic data collection within the public health school system and descriptive analysis of cases reported between October 2020 and June 2021

BACKGROUND: The age-specific distribution of SARS-CoV-2 cases in schools is not well described. Reported statistics reflect the intensity of community transmission while being shaped by biases from age-dependent testing regimes, as well as effective age-specific interventions. A case surveillance system was introduced within the Flemish school and health-prevention network during the 2020–2021 school year. We present epidemiological data of in-school reported cases in pre-, primary and secondary schools identified by the case surveillance system, in conjunction with test data and community cases from October 2020 to June 2021. METHODS: We describe the development of the surveillance system and provide the number of reported cases and standardized rates per grade over time. We calculated absolute and relative differences in case incidence according to school grade (primary: grades 1–6, and secondary: grades 7–12) using grades 7–8 as a comparator, relating them to non-pharmaceutical infection prevention interventions. Cumulative population incidences (IP) stratified by age, province and socioeconomic status (SES) of the school population are presented with their 95% confidence intervals (CI). RESULTS: A total of 59,996 COVID-19 cases were reported in the school surveillance system, with the highest population adjusted IP in grade 11–12 of 7.39% (95%CI 7.24–7.53) and ranging from 2.23% to 6.25% from pre-school through grade 10. Age-specific reductions in mask introduction and in-person teaching were temporally associated with decreased case incidence, while lower pupil SES was associated with an increase in cumulative cases (excess 2,739/100,000 pupils compared to highest SES tertile). Community testing volumes varied more for children compared to adults, with overall higher child test-positivity. Holidays influence capturing of cases by the system, however efficiency increased to above 75% after further automation and integration in existing structures. CONCLUSION: We demonstrate that effective integration of case surveillance within an electronic school health system is feasible, provides valuable data regarding the evolution of an epidemic among schoolchildren, and is an integral component of public health surveillance and pandemic preparedness. The relationship towards community transmission needs careful evaluation because of age-different testing regimens. In the Flemish region, case incidence within schools exhibited an age gradient that was mitigated through grade-specific interventions, though differences by SES remain. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-14250-1

Change in COVID19 outbreak pattern following vaccination in long-term care facilities in Flanders, Belgium

INTRODUCTION: Long term care facilities for elderly (LTCFs) in Europe encountered a high disease burden at the start of the COVID-19 pandemic. Therefore, these facilities were the first to receive COVID-19 vaccines in many European countries. A limited COVID-19 vaccine supply early 2021 resulted in a majority of residents and healthcare workers (HCWs) in LTCFs being vaccinated compared to a minority in the general population. This study exploits this imbalance to assess the efficiency of COVID-19 vaccination in containing outbreaks in LTCFs. METHODS: Exploratory statistics were performed using data from a COVID-19 surveillance system covering all 842 LTCFs in Flanders (the northern region of Belgium). The number and size of COVID-19 outbreaks in LTCFs were compared (1) before and after introducing vaccines and (2) with the status of the pandemic in the general population. Based on individual data from 15 LTCFs, the infection rate and symptoms of vaccinated and unvaccinated residents and HCWs were compared during a COVID-19 outbreak. RESULTS: 95.8% of the residents and 90.9% of the HCWs in Flemish LTCFs were vaccinated before May 30, 2021. Before vaccine introduction, residents in LTCFs were 10 times more likely to test positive for COVID-19 than the general population of Flanders. This ratio reversed after vaccination. Furthermore, after vaccination fewer and shorter outbreaks were observed involving fewer residents. During these outbreaks, vaccinated and unvaccinated residents were equally likely to test positive, but positive vaccinated residents were less likely to develop severe symptoms. In contrast, unvaccinated HCWs were more likely to test positive. CONCLUSION: In the first half of 2021, two-dose vaccination was highly efficient in preventing and containing outbreaks in LTCFs, reducing COVID-19 hospitalizations and deaths. The high likelihood of unvaccinated HCWs to be involved in COVID-19 outbreaks in vaccinated LTCFs emphasizes the importance of vaccinating HCWs

Individual factors influencing COVID-19 vaccine acceptance in between and during pandemic waves (July-December 2020)

BACKGROUND: A year after the start of the COVID-19 outbreak, the global rollout of vaccines gives us hope of ending the pandemic. Lack of vaccine confidence, however, poses a threat to vaccination campaigns. This study aims at identifying individuals’ characteristics that explain vaccine willingness in Flanders (Belgium), while also describing trends over time (July–December 2020). METHODS: The analysis included data of 10 survey waves of the Great Corona Survey, a large-scale online survey that was open to the general public and had 17,722–32,219 respondents per wave. Uni- and multivariable general additive models were fitted to associate vaccine willingness with socio-demographic and behavioral variables, while correcting for temporal and geographical variability. RESULTS: We found 84.2% of the respondents willing to be vaccinated, i.e., respondents answering that they were definitely (61.2%) or probably (23.0%) willing to get a COVID-19 vaccine, while 9.8% indicated maybe, 3.9% probably not and 2.2% definitely not. In Flanders, vaccine willingness was highest in July 2020 (90.0%), decreased over the summer period to 80.2% and started to increase again from late September, reaching 85.9% at the end of December 2020. Vaccine willingness was significantly associated with respondents’ characteristics: previous survey participation, age, gender, province, educational attainment, household size, financial situation, employment sector, underlying medical conditions, mental well-being, government trust, knowing someone with severe COVID-19 symptoms and compliance with restrictive measures. These variables could explain much, but not all, variation in vaccine willingness. CONCLUSIONS: Both the timing and location of data collection influence vaccine willingness results, emphasizing that comparing data from different regions, countries and/or timepoints should be done with caution. To maximize COVID-19 vaccination coverage, vaccination campaigns should focus on (a combination of) subpopulations: aged 31–50, females, low educational attainment, large households, difficult financial situation, low mental well-being and labourers, unemployed and self-employed citizens