COVID-19 pandemic predictions using the modified Bateman SIZ model and observational data for Heidelberg, Germany: Effect of vaccination with a SARS-CoV-2 vaccine, coronavirus testing and application of the Corona-Warn-App

Braun, Peter; Haffner, Steffen; Woodcock, Barry G.

doi:10.5414/cp203846

Cited by 9 publications

(7 citation statements)

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“…11 To illustrate, Australia has an app named COVIDSafe, 12 in which health officials would notify people with previous close contacts to infected people. Similar approach is used by Germany (Corona-Warn-App 13 ) and Singapore (TraceTogether). 14 China was in fact the pioneer in using community-based health QR code system early in mid-February 2020, 15 in which the system relies on health self-declaration, contact tracing in identifying individuals' infection risk.…”

Section: Vaccine Passports With Health Recordsmentioning

confidence: 99%

The way forward after COVID-19 vaccination: vaccine passports with blockchain to protect personal privacy

et al. 2021

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Section: Vaccine Passports With Health Recordsmentioning

confidence: 99%

The way forward after COVID-19 vaccination: vaccine passports with blockchain to protect personal privacy

et al. 2021

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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%

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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“…This can be mandatory since many countries are facing problems with vaccination speed and availability as well as social isolation as well as a severe lack of citizen compliance in many countries. Similarly, in Bateman SIZ analysis, this approach can be observed [52] regarding the uncertainty of models without using predefined parameters for sensitivity and robustness. These uncertain parameters lead the calculations to include new panoramic idea of how to combat COVID-19 without proper pre assumptions, thus giving for the real world skewness observed in predictive methods, a degree of uncertainty worldwide.…”

Section: Discussionmentioning

confidence: 99%

SARS-COV-2: SIR Model Limitations and Predictive Constraints

Telles¹,

Lopes

Franco

2021

Symmetry

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Background: The main purpose of this research is to describe the mathematical asymmetric patterns of susceptible, infectious, or recovered (SIR) model equation application in the light of coronavirus disease 2019 (COVID-19) skewness patterns worldwide. Methods: The research modeled severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) spreading and dissemination patterns sensitivity by redesigning time series data extraction of daily new cases in terms of deviation consistency concerning variables that sustain COVID-19 transmission. The approach opened a new scenario where seasonality forcing behavior was introduced to understand SARS-COV-2 non-linear dynamics due to heterogeneity and confounding epidemics scenarios. Results: The main research results are the elucidation of three birth- and death-forced seasonality persistence phases that can explain COVID-19 skew patterns worldwide. They are presented in the following order: (1) the environmental variables (Earth seasons and atmospheric conditions); (2) health policies and adult learning education (HPALE) interventions; (3) urban spaces (local indoor and outdoor spaces for transit and social-cultural interactions, public or private, with natural physical features (river, lake, terrain). Conclusions: Three forced seasonality phases (positive to negative skew) phases were pointed out as a theoretical framework to explain uncertainty found in the predictive SIR model equations that might diverge in outcomes expected to express the disease’s behaviour.

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COVID-19 pandemic predictions using the modified Bateman SIZ model and observational data for Heidelberg, Germany: Effect of vaccination with a SARS-CoV-2 vaccine, coronavirus testing and application of the Corona-Warn-App

Cited by 9 publications

References 0 publications

The way forward after COVID-19 vaccination: vaccine passports with blockchain to protect personal privacy

The way forward after COVID-19 vaccination: vaccine passports with blockchain to protect personal privacy

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

SARS-COV-2: SIR Model Limitations and Predictive Constraints

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