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Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infectious contacts.

ABSTRACT: Mathematical models have been widely used during the ongoing SARS-CoV-2 pandemic for data interpretation, forecasting, and policy making. However, most models are based on officially reported case numbers, which depend on test availability and test strategies. The time dependence of these factors renders interpretation difficult and might even result in estimation biases. Here, we present a computational modelling framework that allows for the integration of reported case numbers with seroprevalence estimates obtained from representative population cohorts. To account for the time dependence of infection and testing rates, we embed flexible splines in an epidemiological model. The parameters of these splines are estimated, along with the other parameters, from the available data using a Bayesian approach. The application of this approach to the official case numbers reported for Munich (Germany) and the seroprevalence reported by the prospective COVID-19 Cohort Munich (KoCo19) provides first estimates for the time dependence of the under-reporting factor. Furthermore, we estimate how the effectiveness of non-pharmaceutical interventions and of the testing strategy evolves over time. Overall, our results show that the integration of temporally highly resolved and representative data is beneficial for accurate epidemiological analyses.

SUBMITTER: Contento L

PROVIDER: S-EPMC10008049 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infectious contacts.

Contento Lorenzo L Castelletti Noemi N Raimúndez Elba E Le Gleut Ronan R Schälte Yannik Y Stapor Paul P Hinske Ludwig Christian LC Hoelscher Michael M Wieser Andreas A Radon Katja K Fuchs Christiane C Hasenauer Jan J

Epidemics 20230311

Mathematical models have been widely used during the ongoing SARS-CoV-2 pandemic for data interpretation, forecasting, and policy making. However, most models are based on officially reported case numbers, which depend on test availability and test strategies. The time dependence of these factors renders interpretation difficult and might even result in estimation biases. Here, we present a computational modelling framework that allows for the integration of reported case numbers with seropreval ...[more]

PMID: 36931114

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Project description:IntroductionInternational investment in the response to HIV and AIDS has plateaued and its future level is uncertain. With many countries committed to ending the epidemic, it is essential to allocate available resources efficiently over different response periods to maximize impact. The objective of this study is to propose a technique to determine the optimal allocation of funds over time across a set of HIV programmes to achieve desirable health outcomes.MethodsWe developed a technique to determine the optimal time-varying allocation of funds (1) when the future annual HIV budget is pre-defined and (2) when the total budget over a period is pre-defined, but the year-on-year budget is to be optimally determined. We use this methodology with Optima, an HIV transmission model that uses non-linear relationships between programme spending and associated programmatic outcomes to quantify the expected epidemiological impact of spending. We apply these methods to data collected from Zambia to determine the optimal distribution of resources to fund the right programmes, for the right people, at the right time.Results and discussionConsidering realistic implementation and ethical constraints, we estimate that the optimal time-varying redistribution of the 2014 Zambian HIV budget between 2015 and 2025 will lead to a 7.6% (7.3% to 7.8%) decrease in cumulative new HIV infections compared with a baseline scenario where programme allocations remain at 2014 levels. This compares to a 5.1% (4.6% to 5.6%) reduction in new infections using an optimal allocation with constant programme spending that recommends unrealistic programmatic changes. Contrasting priorities for programme funding arise when assessing outcomes for a five-year funding period over 5-, 10- and 20-year time horizons.ConclusionsCountries increasingly face the need to do more with the resources available. The methodology presented here can aid decision-makers in planning as to when to expand or contract programmes and to which coverage levels to maximize impact.

Project description:BackgroundChemoprophylaxis of contacts of infectious tuberculosis (TB) cases is recommended for TB control, particularly in endemic countries, but is hampered by the difficulty to diagnose latent TB infection (LTBI), classically assessed through response to the Tuberculin Skin Test (TST). Interferon-gamma release assays (IGRA) are proposed new tools to diagnose LTBI, but there are limited data on their ability to predict the development of active TB disease. To address this, we investigated the response to TST and IGRA in household contacts of infectious TB cases in a TB high-burden country and the potential correlation with development of TB.Methodology/principal findingsProspective household contacts study conducted in two health centres in Dakar, Senegal. A total of 2679 household contacts of 206 newly detected smear and/or culture positive index TB cases aged 18 years or greater were identified A TST was performed in each contact and an ESAT6/CFP10 ELISPOT assay performed in a random sample of those. Contacts were followed-up for 24 months. TB was diagnosed in 52 contacts, an incidence rate of 9.27/1000 person-years. In univariable analysis, the presence of positive TST (> or = 10 mm) and ELISPOT (>32 SFC/million PBMC) responses at baseline were associated with active TB during follow-up: Rate Ratio [RR] = 2.32 (95%CI:1.12-4.84) and RR = 2.09 (95%CI:0.83-5.31), respectively. After adjustment for age, sex and proximity to index case, adjusted RRs were 1.51 (95%CI:0.71-3.19) and 1.98 (95%CI:0.77-5.09), respectively. Restricting analysis to the 40 microbiologically confirmed cases, the adjusted RR for positive ELISPOT was 3.61 (95%CI:1.03-12.65). The median ELISPOT response in contacts who developed TB was 5-fold greater than in those who did not develop TB (p = 0.02).Conclusions/significanceTST and IGRAs are markers of a contact of the immune system with tubercle bacilli. In a TB endemic area, a high ELISPOT response may reflect increased bacterial replication that may subsequently be associated with development of TB disease and may have a prognostic value. Further longitudinal data are needed to assess whether IGRAs are reliable markers to be used for targeting chemoprophylaxis.

Project description:BackgroundInfectious diseases such as SARS and H1N1 can significantly impact people's lives and cause severe social and economic damages. Recent outbreaks have stressed the urgency of effective research on the dynamics of infectious disease spread. However, it is difficult to predict when and where outbreaks may emerge and how infectious diseases spread because many factors affect their transmission, and some of them may be unknown.MethodsOne feasible means to promptly detect an outbreak and track the progress of disease spread is to implement surveillance systems in regional or national health and medical centres. The accumulated surveillance data, including temporal, spatial, clinical, and demographic information can provide valuable information that can be exploited to better understand and model the dynamics of infectious disease spread. The aim of this work is to develop and empirically evaluate a stochastic model that allows the investigation of transmission patterns of infectious diseases in heterogeneous populations.ResultsWe test the proposed model on simulation data and apply it to the surveillance data from the 2009 H1N1 pandemic in Hong Kong. In the simulation experiment, our model achieves high accuracy in parameter estimation (less than 10.0 % mean absolute percentage error). In terms of the forward prediction of case incidence, the mean absolute percentage errors are 17.3 % for the simulation experiment and 20.0 % for the experiment on the real surveillance data.ConclusionWe propose a stochastic model to study the dynamics of infectious disease spread in heterogeneous populations from temporal-spatial surveillance data. The proposed model is evaluated using both simulated data and the real data from the 2009 H1N1 epidemic in Hong Kong and achieves acceptable prediction accuracy. We believe that our model can provide valuable insights for public health authorities to predict the effect of disease spread and analyse its underlying factors and to guide new control efforts.

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Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infectious contacts.

Publications

Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infectious contacts.

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