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@article{Antulov-Fantulin2022_AccuracyShorttermCOVID19,
title = {On the Accuracy of Short-Term {{COVID-19}} Fatality Forecasts},
author = {{Antulov-Fantulin}, Nino and B{\"o}ttcher, Lucas},
year = {2022},
month = dec,
journal = {BMC Infectious Diseases},
volume = {22},
number = {1},
pages = {1--7},
publisher = {{BioMed Central}},
issn = {1471-2334},
doi = {10.1186/s12879-022-07205-9},
abstract = {Forecasting new cases, hospitalizations, and disease-induced deaths is an important part of infectious disease surveillance and helps guide health officials in implementing effective countermeasures. For disease surveillance in the US, the Centers for Disease Control and Prevention (CDC) combine more than 65 individual forecasts of these numbers in an ensemble forecast at national and state levels. A similar initiative has been launched by the European CDC (ECDC) in the second half of 2021. We collected data on CDC and ECDC ensemble forecasts of COVID-19 fatalities, and we compare them with easily interpretable ``Euler'' forecasts serving as a model-free benchmark that is only based on the local rate of change of the incidence curve. The term ``Euler method'' is motivated by the eponymous numerical integration scheme that calculates the value of a function at a future time step based on the current rate of change. Our results show that simple and easily interpretable ``Euler'' forecasts can compete favorably with both CDC and ECDC ensemble forecasts on short-term forecasting horizons of 1 week. However, ensemble forecasts better perform on longer forecasting horizons. Using the current rate of change in incidences as estimates of future incidence changes is useful for epidemic forecasting on short time horizons. An advantage of the proposed method over other forecasting approaches is that it can be implemented with a very limited amount of work and without relying on additional data (e.g., data on human mobility and contact patterns) and high-performance computing systems.},
copyright = {2022 The Author(s)},
langid = {english}
}
@article{Kucharski2021_SharingSynthesisSustainability,
title = {Sharing, Synthesis and Sustainability of Data Analysis for Epidemic Preparedness in {{Europe}}},
author = {Kucharski, Adam J. and Hodcroft, Emma B. and Kraemer, Moritz U. G.},
year = {2021},
month = oct,
journal = {The Lancet Regional Health \textendash{} Europe},
volume = {9},
publisher = {{Elsevier}},
issn = {2666-7762},
doi = {10.1016/j.lanepe.2021.100215},
langid = {english},
pmid = {34642674}
}
@phdthesis{MarcosAlarcon2021_EstudioModelosAprendizaje,
title = {{Estudio de Modelos de Aprendizaje Autom\'atico Probabil\'istico para la Predicci\'on de casos de Covid-19 en Espa\~na}},
author = {Marcos Alarc{\'o}n, Pablo},
year = {2021},
month = oct,
abstract = {En el presente estudio se va a analizar la efectividad de diferentes modelos de aprendizaje autom\'atico con salida probabil\'istica e inferencia Bayesiana, aplicados a la predicci\'on de casos de Covid-19 en Espa\~na. Para ello, se utilizar\'an los datos y las m\'etricas de evaluaci\'on utilizadas en el European Covid-19 Forecast Hub (a partir de ahora, Hub). Dicho Hub est\'a coordinado por el Centro Europeo de Predicci\'on y Control de Enfermedades y se encarga de coleccionar y combinar predicciones a corto plazo de casos, hospitalizaciones y muertes por Covid-19 en toda Europa (Uni\'on Europea, pa\'ises EFTA y UK), generados por diferentes equipos de modelado independientes, utilizando diferentes tipos de metodolog\'ias [1]. Durante el estudio se analizar\'a el rendimiento de modelos predictivos que utilizan diferentes metodolog\'ias, siguiendo un enfoque incremental en cuanto a la complejidad. El an\'alisis se llevar\'a a cabo analizando, tanto los resultados del entrenamiento de los modelos, como la comparaci\'on de las predicciones generadas por los mismos con aquellas predicciones de los modelos existentes en el Hub, utilizando para ello las medidas de evaluaci\'on con las que se eval\'uan semanalmente las predicciones enviadas al Hub. Los modelos estudiados utilizan \'unicamente datos de casos producidos en el pasado, focalizando el estudio, por tanto, en el componente autoregresivo y de dependencia temporal del modelado. El estudio pone especial atenci\'on en la evaluaci\'on y selecci\'on de modelos de regresi\'on. Por tanto, el alcance del estudio es el de analizar hasta qu\'e punto los modelos de regresi\'on probabil\'istica e inferencia bayesiana pueden alcanzar resultados fiables con respecto a modelos predictivos m\'as sofisticados que utilizan conjuntos de datos ampliados con conceptos de epidemiolog\'ia.},
langid = {spanish},
school = {Universidad Nacional de Educaci\'on a Distancia (Espa\~na). Escuela T\'ecnica Superior de Ingenier\'ia Inform\'atica. Departamento de Inteligencia Artificial},
url = {http://e-spacio.uned.es/fez/view/bibliuned:master-ETSInformatica-ICD-Pmarcos}
}
@article{Parolini2022_ModellingCOVID19Epidemic,
title = {Modelling the {{COVID-19}} Epidemic and the Vaccination Campaign in {{Italy}} by the {{SUIHTER}} Model},
author = {Parolini, Nicola and Dede, Luca and Ardenghi, Giovanni and Quarteroni, Alfio},
year = {2022},
month = mar,
journal = {Infectious Disease Modelling},
issn = {2468-0427},
doi = {10.1016/j.idm.2022.03.002},
abstract = {Several epidemiological models have been proposed to study the evolution of COVID-19 pandemic. In this paper, we propose an extension of the SUIHTER model, to analyse the COVID-19 spreading in Italy, which accounts for the vaccination campaign and the presence of new variants when they become dominant. In particular, the specific features of the variants (e.g. their increased transmission rate) and vaccines (e.g. their efficacy to prevent transmission, hospitalization and death) are modeled, based on clinical evidence. The new model is validated comparing its near-future forecast capabilities with other epidemiological models and exploring different scenario analyses.},
langid = {english},
keywords = {Compartmental model,COVID-19,Forecast,Scenario analyses,Vaccination,Virus variants}
}
@article{Rodriguez2022_DataCentricEpidemic,
doi = {10.48550/ARXIV.2207.09370},
author = {Rodríguez, Alexander and Kamarthi, Harshavardhan and Agarwal, Pulak and Ho, Javen and Patel, Mira and Sapre, Suchet and Prakash, B. Aditya},
keywords = {Machine Learning (cs.LG), Computers and Society (cs.CY), Databases (cs.DB), Quantitative Methods (q-bio.QM), Applications (stat.AP), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Biological sciences, FOS: Biological sciences},
title = {Data-Centric Epidemic Forecasting: A Survey},
journal = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
@article{Fosnaric2022.07.16.22277702,
author = {Fo{\v s}nari{\v c}, Miha and Kamen{\v s}ek, Tina and Gros, Jerneja {\v Z}ganec and {\v Z}ibert, Janez},
title = {Extended compartmental model for modeling COVID-19 epidemic in Slovenia},
elocation-id = {2022.07.16.22277702},
year = {2022},
doi = {10.1101/2022.07.16.22277702},
publisher = {Cold Spring Harbor Laboratory Press},
abstract = {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.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThis work was supported by Slovenian Research Agency under Research Programme P2-0250 (Grant number ARRS-RPROG-JP_COVID19-Prijava/2020/050).Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesI confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data produced are available online at https://covid-19.sledilnik.org/ and https://github.com/sledilnik. Source code of the model described in this study can be found at https://github.com/janezz25/SEIR-C19-SI . https://apps.lusy.fri.uni-lj.si},
journal = {medRxiv}
}
@article{Srivastava2022,
doi = {10.48550/ARXIV.2207.02919},
author = {Srivastava, Ajitesh},
keywords = {Populations and Evolution (q-bio.PE), Physics and Society (physics.soc-ph), Quantitative Methods (q-bio.QM), FOS: Biological sciences, FOS: Biological sciences, FOS: Physical sciences, FOS: Physical sciences},
title = {The Variations of SIkJalpha Model for COVID-19 Forecasting and Scenario Projections},
journal = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
@article {Abbott2022.10.12.22280917,
author = {Sam Abbott and Katharine Sherratt and Nikos Bosse and Hugo Gruson and Johannes Bracher and Sebastian Funk},
title = {Evaluating an epidemiologically motivated surrogate model of a multi-model ensemble},
elocation-id = {2022.10.12.22280917},
year = {2022},
doi = {10.1101/2022.10.12.22280917},
publisher = {Cold Spring Harbor Laboratory Press},
abstract = {Multi-model and multi-team ensemble forecasts have become widely used to generate reliable short-term predictions of infectious disease spread. Notably, various public health agencies have used them to leverage academic disease modelling during the COVID-19 pandemic. However, ensemble forecasts are difficult to interpret and require extensive effort from numerous participating groups as well as a coordination team. In other fields, resource usage has been reduced by training simplified models that reproduce some of the observed behaviour of more complex models. Here we used observations of the behaviour of the European COVID-19 Forecast Hub ensemble combined with our own forecasting experience to identify a set of properties present in current ensemble forecasts. We then developed a parsimonious forecast model intending to mirror these properties. We assess forecasts generated from this model in real time over six months (the 15th of January 2022 to the 19th of July 2022) and for multiple European countries. We focused on forecasts of cases one to four weeks ahead and compared them to those by the European forecast hub ensemble. We find that the surrogate model behaves qualitatively similarly to the ensemble in many instances, though with increased uncertainty and poorer performance around periods of peak incidence (as measured by the Weighted Interval Score). The performance differences, however, seem to be partially due to a subset of time points, and the proposed model appears better probabilistically calibrated than the ensemble. We conclude that our simplified forecast model may have captured some of the dynamics of the hub ensemble, but more work is needed to understand the implicit epidemiological model that it represents.Competing Interest StatementThe authors have declared no competing interest.Funding StatementSA,SF, KS and HG were funded by a Wellcome senior fellowship to SF (210758/Z/18/Z), KS and HG were further funded by an ECDC grant to SF. JB acknowledges support from the Helmholtz Foundation via the SIM-610 CARD Information and Data Science Pilot Project.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesI confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data and code are available here: https://github.com/epiforecasts/simplified-forecaster-evaluation https://doi.org/10.5281/zenodo.7189308},
URL = {https://www.medrxiv.org/content/early/2022/10/13/2022.10.12.22280917},
eprint = {https://www.medrxiv.org/content/early/2022/10/13/2022.10.12.22280917.full.pdf},
journal = {medRxiv}
}
@article{10.1371/journal.pcbi.1010405,
doi = {10.1371/journal.pcbi.1010405},
author = {Bosse, Nikos I. AND Abbott, Sam AND Bracher, Johannes AND Hain, Habakuk AND Quilty, Billy J. AND Jit, Mark AND Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group AND van Leeuwen, Edwin AND Cori, Anne AND Funk, Sebastian},
journal = {PLOS Computational Biology},
publisher = {Public Library of Science},
title = {Comparing human and model-based forecasts of COVID-19 in Germany and Poland},
year = {2022},
month = {09},
volume = {18},
url = {https://doi.org/10.1371/journal.pcbi.1010405},
pages = {1-24},
abstract = {Forecasts based on epidemiological modelling have played an important role in shaping public policy throughout the COVID-19 pandemic. This modelling combines knowledge about infectious disease dynamics with the subjective opinion of the researcher who develops and refines the model and often also adjusts model outputs. Developing a forecast model is difficult, resource- and time-consuming. It is therefore worth asking what modelling is able to add beyond the subjective opinion of the researcher alone. To investigate this, we analysed different real-time forecasts of cases of and deaths from COVID-19 in Germany and Poland over a 1-4 week horizon submitted to the German and Polish Forecast Hub. We compared crowd forecasts elicited from researchers and volunteers, against a) forecasts from two semi-mechanistic models based on common epidemiological assumptions and b) the ensemble of all other models submitted to the Forecast Hub. We found crowd forecasts, despite being overconfident, to outperform all other methods across all forecast horizons when forecasting cases (weighted interval score relative to the Hub ensemble 2 weeks ahead: 0.89). Forecasts based on computational models performed comparably better when predicting deaths (rel. WIS 1.26), suggesting that epidemiological modelling and human judgement can complement each other in important ways.},
number = {9},
}
@article {Morel2022.11.05.22281904,
author = {Jean-David Morel and Jean-Michel Morel and Luis Alvarez},
title = {Learning from the past: a short term forecast method for the COVID-19 incidence curve},
elocation-id = {2022.11.05.22281904},
year = {2023},
doi = {10.1101/2022.11.05.22281904},
publisher = {Cold Spring Harbor Laboratory Press},
abstract = {The COVID-19 pandemy has created a radically new situation where most countries provide raw measurements of their daily incidence and disclose them in real time. This enables new machine learning forecast strategies where the prediction might no longer be based just on the past values of the current incidence curve, but could take advantage of observations in many countries. We present such a simple global machine learning procedure using all past daily incidence trend curves. Each of the 27,418 COVID-19 incidence trend curves in our database contains the values of 56 consecutive days extracted from observed incidence curves across 61 world regions and countries. Given a current incidence trend curve observed over the past four weeks, its forecast in the next four weeks is computed by matching it with the first four weeks of all samples, and ranking them by their similarity to the query curve. Then the 28 days forecast is obtained by a statistical estimation combining the values of the 28 last observed days in those similar samples. Using comparison performed by the European Covid-19 Forecast Hub with the current state of the art forecast methods, we verify that the proposed global learning method, EpiLearn, compares favorably to methods forecasting from a single past curve.Author summary Forecasting the short time evolution of the COVID-19 daily incidence is a key issue in the epidemic decision making policy. We propose a machine learning method which forecasts the future values of the daily incidence trend based on the evolution of other incidence trend curves that were similar to the current one in the past. Using comparison performed by the European Covid-19 Forecast Hub with the current state of the art forecast methods, we verify that the proposed global learning method, EpiLearn compares favorably to methods that forecast from a single past curve.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThis study wa partially funded by Kayrros, Inc.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesI confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data produced are available online at OurWorldInData.org https://github.com/owid/covid-19-data/tree/master/public/data},
URL = {https://www.medrxiv.org/content/early/2023/04/25/2022.11.05.22281904},
eprint = {https://www.medrxiv.org/content/early/2023/04/25/2022.11.05.22281904.full.pdf},
journal = {medRxiv}
}
@article{KULAH2023119034,
title = {COVID-19 forecasting using shifted Gaussian Mixture Model with similarity-based estimation},
journal = {Expert Systems with Applications},
volume = {214},
pages = {119034},
year = {2023},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2022.119034},
url = {https://www.sciencedirect.com/science/article/pii/S0957417422020528},
author = {Emre Külah and Yusuf Mücahit Çetinkaya and Arif Görkem Özer and Hande Alemdar},
keywords = {COVID-19, Gaussian mixture models, Time-series data, Similarity-based estimation, Trend similarity score},
abstract = {The COVID-19 pandemic has caused a pronounced disturbance in the social environments and economies of many countries worldwide. Credible forecasting methods to predict the pandemic’s progress can allow countries to control the disease’s spread and decrease the number of severe cases. This study presents a novel approach, called the Shifted Gaussian Mixture Model with Similarity-based Estimation (SGSE), that forecasts the future of a specific country’s daily new case values by examining similar behavior in other countries. The model uses daily new case values collected since the pandemic began and finds countries with similar trends using a specific time offset. The daily new case values data between the first day and (today−N)th day are transformed by employing the Gaussian Mixture Model (GMM) and, subsequently, a new vector of features is obtained for each country. Using these feature vectors, countries that show similar statistics in the past are found for any forecasted country. The future of the corresponding country is forecasted by taking the mean of the time-series plots after the offset points of similar countries are calculated. A brand new metric called a trend similarity score, which calculates the similarity between forecasted and actual values is also presented in this study. While the SGSE trend similarity score median varies between 0.903–0.947, based on the selection of the distance metric, the ARIMA model yields only 0.642. The performance of the SGSE was compared in seven European countries using four different public projects submitted to The European COVID-19 Forecast Hub. The SGSE gives the most accurate forecasts compared to all other models. The test sets’ results show that trends and plateaus are predicted accurately for many countries.}
}
@article{
doi:10.1073/pnas.2112656119,
author = {Ekaterina Krymova and Benjamín Béjar and Dorina Thanou and Tao Sun and Elisa Manetti and Gavin Lee and Kristen Namigai and Christine Choirat and Antoine Flahault and Guillaume Obozinski },
title = {Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide},
journal = {Proceedings of the National Academy of Sciences},
volume = {119},
number = {32},
pages = {e2112656119},
year = {2022},
doi = {10.1073/pnas.2112656119},
URL = {https://www.pnas.org/doi/abs/10.1073/pnas.2112656119},
eprint = {https://www.pnas.org/doi/pdf/10.1073/pnas.2112656119},
abstract = {Since the beginning of the COVID-19 pandemic, many dashboards have emerged as useful tools to monitor its evolution, inform the public, and assist governments in decision-making. Here, we present a globally applicable method, integrated in a daily updated dashboard that provides an estimate of the trend in the evolution of the number of cases and deaths from reported data of more than 200 countries and territories, as well as 7-d forecasts. One of the significant difficulties in managing a quickly propagating epidemic is that the details of the dynamic needed to forecast its evolution are obscured by the delays in the identification of cases and deaths and by irregular reporting. Our forecasting methodology substantially relies on estimating the underlying trend in the observed time series using robust seasonal trend decomposition techniques. This allows us to obtain forecasts with simple yet effective extrapolation methods in linear or log scale. We present the results of an assessment of our forecasting methodology and discuss its application to the production of global and regional risk maps.}
}
@article{https://doi.org/10.1002/bimj.202200054,
author = {Giudici, Paolo and Tarantino, Barbara and Roy, Arkaprava},
title = {Bayesian time-varying autoregressive models of COVID-19 epidemics},
journal = {Biometrical Journal},
volume = {65},
number = {1},
pages = {2200054},
keywords = {Bayesian models, B-splines, COVID-19 disease, NPI covariates, PARX models},
doi = {https://doi.org/10.1002/bimj.202200054},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.202200054},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/bimj.202200054},
abstract = {Abstract The COVID-19 pandemic has highlighted the importance of reliable statistical models which, based on the available data, can provide accurate forecasts and impact analysis of alternative policy measures. Here we propose Bayesian time-dependent Poisson autoregressive models that include time-varying coefficients to estimate the effect of policy covariates on disease counts. The model is applied to the observed series of new positive cases in Italy and in the United States. The results suggest that our proposed models are capable of capturing nonlinear growth of disease counts. We also find that policy measures and, in particular, closure policies and the distribution of vaccines, lead to a significant reduction in disease counts in both countries.},
year = {2023}
}
@Article{Rodiah2023,
author = {Rodiah, Isti and Vanella, Patrizio and Kuhlmann, Alexander
and Jaeger, Veronika K. and Harries, Manuela and Krause,
Gerard and Karch, Andre and Bock, Wolfgang and Lange, Berit},
title = {Age-specific contribution of contacts to transmission of
SARS-CoV-2 in Germany},
journal = {European Journal of Epidemiology},
year = 2023,
volume = 38,
number = 1,
month = {Jan},
pages = {39–58},
issn = {1573-7284},
doi = {10.1007/s10654-022-00938-6},
url = {http://dx.doi.org/10.1007/s10654-022-00938-6},
publisher = {Springer Science and Business Media LLC}
}
@article {M{\"u}ller2023.03.31.535072,
author = {Sebastian A. M{\"u}ller and Sydney Paltra and Jakob Rehmann and Kai Nagel and Tim O.F. Conrad},
title = {Explicit Modelling of Antibody Levels for Infectious Disease Simulations in the Context of SARS-CoV-2},
elocation-id = {2023.03.31.535072},
year = {2023},
doi = {10.1101/2023.03.31.535072},
publisher = {Cold Spring Harbor Laboratory},
abstract = {Measurable levels of immunoglobulin G antibodies develop after infections with and vaccinations against SARS-CoV-2. These antibodies are temporarily dynamic; due to waning, antibody levels will drop below detection thresholds over time. As a result, epidemiological studies could underestimate population protection, given that antibodies are a marker for protective immunity.During the COVID-19 pandemic, multiple models predicting infection dynamics were used by policymakers to plan public health policies. Explicitly integrating antibody and waning effects into the models is crucial for reliable calculations of individual infection risk. However, only few approaches have been suggested that explicitly treat these effects.This paper presents a methodology that explicitly models antibody levels and the resulting protection against infection for individuals within an agent-based model. This approach can be integrated in general frameworks, allowing complex population studies with explicit antibody and waning effects. We demonstrate the usefulness of our model in two use cases.Competing Interest StatementThe authors have declared no competing interest.},
URL = {https://www.biorxiv.org/content/early/2023/03/31/2023.03.31.535072},
eprint = {https://www.biorxiv.org/content/early/2023/03/31/2023.03.31.535072.full.pdf},
journal = {bioRxiv}
}
@Inbook{Mejova2023,
author="Mejova, Yelena",
editor="Bertoni, Eleonora
and Fontana, Matteo
and Gabrielli, Lorenzo
and Signorelli, Serena
and Vespe, Michele",
title="Digital Epidemiology",
bookTitle="Handbook of Computational Social Science for Policy",
year="2023",
publisher="Springer International Publishing",
address="Cham",
pages="279--303",
abstract="Computational social science has had a profound impact on the study of health and disease, mainly by providing new data sources for all of the primary Ws---what, who, when, and where---in order to understand the final ``why'' of disease. Anonymized digital trace data bring a new level of detail to contact networks, search engine and social media logs allow for the now-casting of symptoms and behaviours, and media sharing informs the formation of attitudes pivotal in health decision-making. Advances in computational methods in network analysis, agent-based modelling, as well as natural language processing, data mining, and time series analysis allow both the extraction of fine-grained insights and the construction of abstractions over the new data sources. Meanwhile, numerous challenges around bias, privacy, and ethics are being negotiated between data providers, academia, the public, and policymakers in order to ensure the legitimacy of the resulting insights and their responsible incorporation into the public health decision-making. This chapter outlines the latest research on the application of computational social science to epidemiology and the data sources and computational methods involved and spotlights ongoing efforts to address the challenges in its integration into policymaking.",
isbn="978-3-031-16624-2",
doi="10.1007/978-3-031-16624-2_15",
url="https://doi.org/10.1007/978-3-031-16624-2_15"
}