Challenges for mathematical epidemiological modelling

Crépey, Pascal; Noël, Harold; Alizon, Samuel

doi:10.1016/j.accpm.2022.101053

Cited by 10 publications

(6 citation statements)

References 19 publications

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“…The moment the interventions were included in the model, the predictions became more accurate. Errors both at the onset and near the peak of the waves are therefore to be expected and are well known in epidemiological modeling 30 – 32 .…”

Section: Discussionmentioning

confidence: 93%

Extended compartmental model for modeling COVID-19 epidemic in Slovenia

Fošnarič

Kamenšek

Gros

et al. 2022

Sci Rep

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In the absence of a systematic approach to epidemiological modeling in Slovenia, various isolated mathematical epidemiological models emerged shortly after the outbreak of the COVID-19 epidemic. We present an epidemiological model adapted to the COVID-19 situation in Slovenia. The standard SEIR model was extended to distinguish between age groups, symptomatic or asymptomatic disease progression, and vaccinated or unvaccinated populations. Evaluation of the model forecasts for 2021 showed the expected behavior of epidemiological modeling: our model adequately predicts the situation up to 4 weeks in advance; the changes in epidemiologic dynamics due to the emergence of a new viral variant in the population or the introduction of new interventions cannot be predicted by the model, but when the new situation is incorporated into the model, the forecasts are again reliable. Comparison with ensemble forecasts for 2022 within the European Covid-19 Forecast Hub showed better performance of our model, which can be explained by a model architecture better adapted to the situation in Slovenia, in particular a refined structure for vaccination, and better parameter tuning enabled by the more comprehensive data for Slovenia. Our model proved to be flexible, agile, and, despite the limitations of its compartmental structure, heterogeneous enough to provide reasonable and prompt short-term forecasts and possible scenarios for various public health strategies. The model has been fully operational on a daily basis since April 2020, served as one of the models for decision-making during the COVID-19 epidemic in Slovenia, and is part of the European Covid-19 Forecast Hub.

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Section: Discussionmentioning

confidence: 93%

Extended compartmental model for modeling COVID-19 epidemic in Slovenia

Fošnarič

Kamenšek

Gros

et al. 2022

Sci Rep

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“…The third possibility is that the sample case itself is accidental. There are occasional statistical variations in the sampled samples, and the variation of medical data may lead to wrong conclusions [30]. This situation deserves attention and discussion in future research.…”

Section: Discussionmentioning

confidence: 99%

Mathematical Analysis of the Healthcare Treatment of 215 Patients with Coronary Heart Disease

Tao

Sun

Qin

et al. 2022

Cellular Microbiology

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The main risk factors for CHD and the comorbidity include hyperlipidemia (HL), hypertension, smoking, dietary factors, and genetic factors. In this work, 215 patients with coronary heart disease, including 128 males and 87 females, were analyzed for a better understanding of the related clinical pharmacology. Nonparametric test, analysis of variance, chi-square test, correlation analysis, and other methods were used to sort out the data. From the analysis, there are significant differences in age among different gender samples. The incidence of coronary heart disease in men is five years younger than that in women. The sample pairs from different regions showed differences in the presence of family history of diabetes, indicating that a series of patients in some regions concentrated on the disease status of family history of diabetes. Age has a significant positive effect on cardiac functional classification. The older you are, the larger the cardiac functional classification is and the worse the cardiac function is. Age was negatively correlated with VTE score, diastolic blood pressure, CAR, TG, neutrophil, and TC. The older you are, the lower these six values are. Samples of different types of CHD showed significant differences in the presence of comorbidity and family history of CHD. The most significant are unstable angina pectoris and ischemic cardiomyopathy. Samples of different CHD types showed significant effects on VTE score, creatine kinase, low-density lipoprotein cholesterol (LDL⁃C), and lactate dehydrogenase. The highest lactate dehydrogenase is ischemic cardiomyopathy. The highest LDL cholesterol is ST-segment elevation angina. The highest creatine kinase is ischemic cardiomyopathy. The VTE score was the highest for ischemic cardiomyopathy, followed by non-ST-segment elevation angina. Samples taken with or without lipid-lowering drugs showed significant differences in lactate dehydrogenase, creatinine, and TC. There was a significant positive correlation between VTE scores and lactate dehydrogenase, myoglobin, and creatine kinase. High VTE score indicates high lactate dehydrogenase, myoglobin, and creatine kinase. TC has a significant positive correlation with HDL⁃C and TG, respectively. Higher TC values indicate higher HDL⁃C and TG values.

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“…Epidemiological modelling provides reliable and objective information for decision-makers to consider the different options. However, as pointed out by Crepey et al [3] , “making the choice remains a political decision”. In fact, the decision-making process not only considers the need to face the pandemic and to take the best possible care of our patients but must also consider other realities: social consequences, psychological consequences, consequences on the management of non-COVID-19 diseases, etc.…”

Section: Discussionmentioning

confidence: 99%

“…The COVID-19 pandemic has certainly been exceptional, but it is not the first global health crisis in our modern history. Indeed, the H1N1 pandemic in 2009 [1] and the Ebola epidemic in West Africa in 2013–2016 [2] were recent opportunities to increase the interest in epidemiological modelling, as mentioned by Crépey et al in their contribution to this issue [3] .…”

Section: Toward An Interventional Epidemiology?mentioning

confidence: 99%