Modelling the SARS-CoV-2 outbreak: Assessing the usefulness of protective measures to reduce the pandemic at population level

Colomer, M. Angels; Margalida, Antoni; Alòs, Francesc; Oliva‐Vidal, Pilar; Vilella, Anna; Fraile, Lorenzo

doi:10.1016/j.scitotenv.2021.147816

Cited by 6 publications

(4 citation statements)

References 53 publications

(51 reference statements)

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“…Regarding the operational characteristics and accessibility to the databases and models employed in the included studies, it was noted that only 16.3% of the references did not engage in mathematical development of the proposed model [ 18 , 20 , 30 , 40 , 43 , 52 , 59 , 62 , 81 , 83 , 90 , 120 , 121 , 131 , 137 , 142 , 151 , 154 , 157 , 167 , 170 , 172 , 173 , 177 , 178 , 188 , 189 , 192 , 194 , 202 , 205 – 208 , 211 , 215 , 217 , 222 , 242 , 245 , 247 – 250 , 254 , 273 , 277 , 285 , 295 , 301 , 305 , 324 , 332 , 334 , 339 , 340 , 351 , 358 , 365 , 372 , 385 , 387 , 390 , 393 , 404 , 417 , 426 ]. The main software utilized was R, accounting for 19.7% of cases [ 27 ,…”

Section: Resultsmentioning

confidence: 99%

“…On the other hand, 117 (28.5%) references considered the reinfection variable [ 17 , 22 , 28 , 32 , 36 – 38 , 44 , 46 , 59 – 61 , 63 , 70 , 72 , 77 , 79 , 92 , 94 , 103 , 105 , 108 , 122 , 125 , 128 , 129 , 138 , 140 , 145 , 146 , 149 , 153 , 156 , 157 , 163 – 165 , 168 , 172 , 175 , 176 , 178 , 185 , 190 , 192 , 198 , 201 , 217 – 219 , 223 , 226 , 230 , 231 , 233 , 241 , 243 , 254 , 258 , 264 , 265 , 267 , 269 , 276 , 277 , 281 , 283 , 285 , 291 , 293 , 296 ,…”

Section: Resultsunclassified

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Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review

Espinosa,

Mora,

Sanabria

et al. 2024

Syst Rev

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Background The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19). Methodology PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542. Findings In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias. Interpretation The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsunclassified

Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review

Espinosa,

Mora,

Sanabria

et al. 2024

Syst Rev

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“…One of the main channels of virus transmission is through coming into contact with other people when participating in activities or travelling (e.g., Mossong et al, 2008 ; Akakzia et al, 2007 ; Latsuzbaia et al, 2020 ; Li et al, 2020 ; Colomer et al, 2021 ; Mikszewski et al, 2022 ; Zhang et al, 2021a ). To contain the spread of COVID-19, various non-pharmaceutical interventions have been implemented.…”

Section: Introductionmentioning

confidence: 99%

Dependence analysis of social contact behaviors under the impacts of COVID-19 based on a copula approach

Ding

Zhang

Feng

et al. 2023

Science of The Total Environment

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“…This ability to implement heterogeneity is indeed essential for COVID-19 since it is found in individual risk factors (e.g., age, hypertension, diabetes), contact patterns (e.g., social networks in the community or work settings), behaviors (e.g., willingness to be vaccinated), and spatial aspects (e.g., access to healthcare) [ 24 , 25 , 26 ]. Although ABMs for COVID-19 have been extremely varied in purpose and design [ 27 ], their design broadly followed three stages in the pandemic [ 28 ]: ABMs started with a handful of stages (e.g., susceptible, exposed, infected, recovered) and examined which non-pharmaceutical interventions would have the strongest effect [ 29 , 30 , 31 ], then ABMs were created to support vaccine-related studies (e.g., who should be vaccinated, where to place the centers and how to staff them) [ 32 , 33 , 34 ] and lastly, the current wave of studies where repeated boosters account for waning immunity [ 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

A Scoping Review of Three Dimensions for Long-Term COVID-19 Vaccination Models: Hybrid Immunity, Individual Drivers of Vaccinal Choice, and Human Errors

2022

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The virus that causes COVID-19 changes over time, occasionally leading to Variants of Interest (VOIs) and Variants of Concern (VOCs) that can behave differently with respect to detection kits, treatments, or vaccines. For instance, two vaccination doses were 61% effective against the BA.1 predominant variant, but only 24% effective when BA.2 became predominant. While doses still confer protection against severe disease outcomes, the BA.5 variant demonstrates the possibility that individuals who have received a few doses built for previous variants can still be infected with newer variants. As previous vaccines become less effective, new ones will be released to target specific variants and the whole process of vaccinating the population will restart. While previous models have detailed logistical aspects and disease progression, there are three additional key elements to model COVID-19 vaccination coverage in the long term. First, the willingness of the population to participate in regular vaccination campaigns is essential for long-term effective COVID-19 vaccination coverage. Previous research has shown that several categories of variables drive vaccination status: sociodemographic, health-related, psychological, and information-related constructs. However, the inclusion of these categories in future models raises questions about the identification of specific factors (e.g., which sociodemographic aspects?) and their operationalization (e.g., how to initialize agents with a plausible combination of factors?). While previous models separately accounted for natural- and vaccine-induced immunity, the reality is that a significant fraction of individuals will be both vaccinated and infected over the coming years. Modeling the decay in immunity with respect to new VOCs will thus need to account for hybrid immunity. Finally, models rarely assume that individuals make mistakes, even though this over-reliance on perfectly rational individuals can miss essential dynamics. Using the U.S. as a guiding example, our scoping review summarizes these aspects (vaccinal choice, immunity, and errors) through ten recommendations to support the modeling community in developing long-term COVID-19 vaccination models.

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Modelling the SARS-CoV-2 outbreak: Assessing the usefulness of protective measures to reduce the pandemic at population level

Cited by 6 publications

References 53 publications

Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review

Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review

Dependence analysis of social contact behaviors under the impacts of COVID-19 based on a copula approach

A Scoping Review of Three Dimensions for Long-Term COVID-19 Vaccination Models: Hybrid Immunity, Individual Drivers of Vaccinal Choice, and Human Errors

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