Multiscale model for forecasting Sabin 2 vaccine virus household and community transmission

Wong, Waichi; Haque, Rashidul; Platts-Mills, James A; Saha, Parimalendu; Aziz, Asma; Ahmed, Tahmina; Islam, Md Ohedul; Mj, Uddin; Bandyopadhyay, Ananda S; Yunus, Mohammed; Zaman, Khalequ; Taniuchi, Mami

doi:10.1371/journal.pcbi.1009690

Cited by 7 publications

(15 citation statements)

References 31 publications

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“…Of these, 24/29 (82.8%) actually shared code, 29/33 (87.9%) actually shared data and 5/6 (83.3%) were indeed registered. The papers that used registration were two malaria models [24,25], one vector model [26] (which focused on malaria vectors) one polio (Sabin 2 virus [27]) model and one rotavirus model [28]. The majority were from 2021 [24,26,27] and were also malaria models (two malaria and one vector that was essentially malaria [24][25][26]).…”

Section: Manual Validationmentioning

confidence: 99%

A meta-epidemiological assessment of transparency indicators of infectious disease models

Zavalis

Ioannidis

2022

PLoS ONE

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Mathematical models have become very influential, especially during the COVID-19 pandemic. Data and code sharing are indispensable for reproducing them, protocol registration may be useful sometimes, and declarations of conflicts of interest (COIs) and of funding are quintessential for transparency. Here, we evaluated these features in publications of infectious disease-related models and assessed whether there were differences before and during the COVID-19 pandemic and for COVID-19 models versus models for other diseases. We analysed all PubMed Central open access publications of infectious disease models published in 2019 and 2021 using previously validated text mining algorithms of transparency indicators. We evaluated 1338 articles: 216 from 2019 and 1122 from 2021 (of which 818 were on COVID-19); almost a six-fold increase in publications within the field. 511 (39.2%) were compartmental models, 337 (25.2%) were time series, 279 (20.9%) were spatiotemporal, 186 (13.9%) were agent-based and 25 (1.9%) contained multiple model types. 288 (21.5%) articles shared code, 332 (24.8%) shared data, 6 (0.4%) were registered, and 1197 (89.5%) and 1109 (82.9%) contained COI and funding statements, respectively. There was no major changes in transparency indicators between 2019 and 2021. COVID-19 articles were less likely to have funding statements and more likely to share code. Further validation was performed by manual assessment of 10% of the articles identified by text mining as fulfilling transparency indicators and of 10% of the articles lacking them. Correcting estimates for validation performance, 26.0% of papers shared code and 41.1% shared data. On manual assessment, 5/6 articles identified as registered had indeed been registered. Of articles containing COI and funding statements, 95.8% disclosed no conflict and 11.7% reported no funding. Transparency in infectious disease modelling is relatively low, especially for data and code sharing. This is concerning, considering the nature of this research and the heightened influence it has acquired.

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Section: Manual Validationmentioning

confidence: 99%

A meta-epidemiological assessment of transparency indicators of infectious disease models

Zavalis

Ioannidis

2022

PLoS ONE

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“…Of these, 24/29 (82.8%) actually shared code, 29/33 (87.9%) actually shared data and 5/6 (83.3%) were indeed registered. The papers that used registration were two malaria models(14,15), one vector model (16) (which focused on malaria vectors) one polio (Sabin 2 virus (17)) model and one rotavirus model(18). The majority were from 2021(14,16,17) and were also malaria models (two malaria and one vector that was essentially malaria (14–16)) the majority we also classified as spatiotemporal (14–16).…”

Section: Resultsmentioning

confidence: 99%

“…The papers that used registration were two malaria models(14,15), one vector model (16) (which focused on malaria vectors) one polio (Sabin 2 virus (17)) model and one rotavirus model(18). The majority were from 2021(14,16,17) and were also malaria models (two malaria and one vector that was essentially malaria (14–16)) the majority we also classified as spatiotemporal (14–16). Finally, of the 120 articles (10%) that text mining found that they contained a COI statements, there was indeed a placeholder for this ststement in all articles, but the vast majority of the statements (115 (95.8%)) disclosed no conflict at all.…”

Section: Resultsmentioning

confidence: 99%

A meta-epidemiological assessment of transparency indicators of infectious disease models

Zavalis

Ioannidis

2022

Preprint

View full text Add to dashboard Cite

Mathematical models have become very influential, especially during the COVID-19 pandemic. Data and code sharing are indispensable for reproducing them, protocol registration may be useful sometimes, and declarations of conflicts of interest (COIs) and of funding are quintessential for transparency. Here, we evaluated these features in publications of infectious disease-related models and assessed whether there were differences before and during the COVID-19 pandemic and for COVID-19 models versus models for other diseases. We analysed all PubMed Central open access publications of infectious disease models published in 2019 and 2021 using previously validated text mining algorithms of transparency indicators. We evaluated 1338 articles: 216 from 2019 and 1122 from 2021 (of which 818 were on COVID-19); almost a six-fold increase in publications within the field. 511 (39.2%) were compartmental models, 337 (25.2%) were time series, 279 (20.9%) were spatiotemporal, 186 (13.9%) were agent-based and 25 (1.9%) contained multiple model types. 288 (21.5%) articles shared code, 332 (24.8%) shared data, 6 (0.4%) were registered, and 1197 (89.5%) and 1109 (82.9%) contained COI and funding statements, respectively. There was no major changes in transparency indicators between 2019 and 2021. COVID-19 articles were less likely to have funding statements and more likely to share code. Manual assessment of 10% of the articles that were identified by the text mining algorithms as fulfilling transparency indicators showed that 24/29 (82.8%) actually shared code, 29/33 (87.9%) actually shared data; and all had COI and funding statements, but 95.8% disclosed no conflict and 11.7% reported no funding. On manual assessment, 5/6 articles identified as registered had indeed been registered. Transparency in infectious disease modelling is relatively low, especially for data and code sharing. This is concerning, considering the nature of this research and the heightened influence it has acquired.

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“…For a mechanistic model of oral poliovirus-related vaccine virus transmission risk, optimal model parameters were identified with Latin hypercube sampling and a pseudo-likelihood function to minimize the discrepancy between simulated and observed data (Famulare et al. 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Integration of Heterogeneous Biological Data in Multiscale Mechanistic Model Calibration: Application to Lung Adenocarcinoma

Palgen¹,

Perrillat-Mercerot²,

Ceres³

et al. 2022

Acta Biotheor

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Mechanistic models are built using knowledge as the primary information source, with well-established biological and physical laws determining the causal relationships within the model. Once the causal structure of the model is determined, parameters must be defined in order to accurately reproduce relevant data. Determining parameters and their values is particularly challenging in the case of models of pathophysiology, for which data for calibration is sparse. Multiple data sources might be required, and data may not be in a uniform or desirable format. We describe a calibration strategy to address the challenges of scarcity and heterogeneity of calibration data. Our strategy focuses on parameters whose initial values cannot be easily derived from the literature, and our goal is to determine the values of these parameters via calibration with constraints set by relevant data. When combined with a covariance matrix adaptation evolution strategy (CMA-ES), this step-by-step approach can be applied to a wide range of biological models. We describe a stepwise, integrative and iterative approach to multiscale mechanistic model calibration, and provide an example of calibrating a pathophysiological lung adenocarcinoma model. Using the approach described here we illustrate the successful calibration of a complex knowledge-based mechanistic model using only the limited heterogeneous datasets publicly available in the literature.

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Multiscale model for forecasting Sabin 2 vaccine virus household and community transmission

Cited by 7 publications

References 31 publications

A meta-epidemiological assessment of transparency indicators of infectious disease models

A meta-epidemiological assessment of transparency indicators of infectious disease models

A meta-epidemiological assessment of transparency indicators of infectious disease models

Integration of Heterogeneous Biological Data in Multiscale Mechanistic Model Calibration: Application to Lung Adenocarcinoma

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