Successive Pandemic Waves with Different Virulent Strains and the Effects of Vaccination for SARS-CoV-2

Silva, Alcides Castro e; Bernardes, Américo Tristão; Barbosa, Eduardo Augusto Gonçalves; Chagas, Igor Aparecido Santana das; Dáttilo, Wesley; Reis, Alexandre Barbosa; Ribeiro, Sérvio Pontes

doi:10.3390/vaccines10030343

Cited by 6 publications

(10 citation statements)

References 74 publications

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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%

“…Sex-related data were incorporated into mathematical modeling in 23 articles (5.6%) [ 59 , 68 , 74 , 84 , 95 , 165 , 170 , 174 , 203 , 227 , 235 , 260 , 263 , 285 , 301 , 305 , 334 , 336 , 367 , 377 , 388 , 397 , 426 ]. Additionally, 6 articles (1.5%) included information related to ethnicity [ 59 , 137 , 155 , 235 , 367 , 388 ], and 15 articles (3.6%) included vulnerable groups [ 63 , 64 , 71 , 81 , 177 , 291 , 331 , 374 , 378 , 380 , 382 , 404 , 408 , 409 , 412 ] (see Table 4 ).…”

Section: Resultsmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

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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show abstract

“…Well established knowledge since pioneering work of May & Andersen [32], and widely demonstrated along the COVID-19 pandemic, transmissibility is not related, or even is inversely related, to virulence. Contrasting 2003 SARS epidemic to the most recent SARS-CoV-2 pandemic, it is clear that a virulent variant/genotype will decrease its incidence in the host population, while those genotypes which are less virulent may evolve towards forms which are more transmissible, thus, becoming highly incident, even though more difficult to detect due to low hospitalization [33][34][35][36][37] Even though virulence of dengue is expected to be preserved along time due to the insect vector phase, especially considering vertical transmission [38], a newly arriving serotype must cause more severe disease but not further dissemination. On the contrary, the fast hospitalization removes a host to the transmission cycle.…”

Section: Discussionmentioning

confidence: 99%

Impact of Climate on the Expansion of Dengue Fever in an Endemic Urban Area

Cruz,

Ribeiro,

Duarte

et al. 2023

Preprint

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Dengue is one of the most important arboviruses worldwide, with currently approximately 2.5 billion people at risk of becoming infected, mainly in tropical countries. The present study evaluated the influence of climatic variables on the incidence of dengue fever in an endemic urban area. A time series study of cases was carried out with all confirmed cases of dengue fever in the municipality of Belo Horizonte from 2001 to 2021. The climatic explanatory variables were obtained from the National Institute of Meteorology. The analysis was performed using a generalized linear regression model with three scenarios: a no-lag model, 1-month lag model (1 month earlier) and 2-month lag model (2 months earlier). In the no-lag model, the maximum temperature was associated with an increase in the monthly incidence of dengue (IRR: 1.5). In the 1- and 2-month lag models, the monthly average temperature was associated with an increase in incidence (IRR: 1.9 in both). Our results suggest that temperature contributed to the increase in the incidence of dengue, and an increase of 1 °C could increase the incidence of the disease by approximately 90%. Thus, temperature monitoring can direct efforts towards prevention and control of the disease.

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“…In the absence of global vaccination, simulation experts have predicted continuous waves of COVID-19 infections [ 107 ]. This is now a reality, and most Americans understand that COVID-19 is here to stay [ 1 ].…”

Section: Discussionmentioning

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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Successive Pandemic Waves with Different Virulent Strains and the Effects of Vaccination for SARS-CoV-2

Cited by 6 publications

References 74 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

Impact of Climate on the Expansion of Dengue Fever in an Endemic Urban Area

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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