Design of a Simulation Model for the Diagnosis of Classical Swine Fever Virus in Ecuadorian Farms DOI Open Access

Cristian Inca,

Carlos Bladimir Velasco Moyano, Ángel Isidro Mena Nieto

и другие.

WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE, Год журнала: 2024, Номер 21, С. 345 - 355

Опубликована: Ноя. 5, 2024

Classical swine fever (CSF) is a disease that slows down animal production and international trade; therefore, its identification key in pig farms to take the relevant health measures. Therefore, objective of this research was design Susceptible-Exposed-Infected-Recovered (SEIR) simulation model carry out epidemiological modeling for outbreaks classical Sierra Region Ecuador, using Python software historical data on incidences provinces Ecuadorian highlands, considering variables population, initial number exposed pigs, infected, pigs removed, contagion rate (α), transmission (β), recovery (γ). The results show SEIR allowed us determine population susceptible (healthy) decreases over time until reaching zero. This decrease susceptibility occurred during first 15 days, which shows necessary infect entire with an infected person. increases days total infection process lasts then decreases. It also identified throughout these five years analysis past, it has been increasing from 2015 2019, hurt yields productivity mountains.

Язык: Английский

Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty DOI Creative Commons
Emily Howerton, Lucie Contamin, Luke C. Mullany

и другие.

Nature Communications, Год журнала: 2023, Номер 14(1)

Опубликована: Ноя. 20, 2023

Our ability to forecast epidemics far into the future is constrained by many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify state critical epidemic drivers. Since December 2020, U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams make months ahead SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function both scenario validity model calibration. We show remained close reality for 22 weeks on average before arrival unanticipated variants invalidated key assumptions. An ensemble participating models preserved variation between (using linear opinion pool method) was consistently more reliable than any single in periods valid assumptions, while projection interval coverage near target levels. were used guide pandemic response, illustrating value collaborative hubs

Язык: Английский

Процитировано

32
Characterising information gains and losses when collecting multiple epidemic model outputs DOI Creative Commons
Katharine Sherratt, Ajitesh Srivastava, Kylie E. C. Ainslie

и другие.

Epidemics, Год журнала: 2024, Номер 47, С. 100765 - 100765

Опубликована: Март 27, 2024

Collaborative comparisons and combinations of epidemic models are used as policy-relevant evidence during outbreaks. In the process collecting multiple model projections, such collaborations may gain or lose relevant information. Typically, modellers contribute a probabilistic summary at each time-step. We compared this to directly simulated trajectories. aimed explore information on key quantities; ensemble uncertainty; performance against data, investigating potential continuously from single cross-sectional collection results. July 2022 projections European COVID-19 Scenario Modelling Hub. Five modelling teams projected incidence in Belgium, Netherlands, Spain. by incidence, peaks, cumulative totals. created drawn all trajectories, ensembles median across model's quantiles, linear opinion pool. measured predictive accuracy individual trajectories observations, using weighted ensemble. repeated sequentially increasing weeks observed data. evaluated these reflect with varying By modelled we showed characteristics. Trajectories contained right-skewed distribution well represented an pool, but not models' quantile intervals. Ensembles typically retained range plausible over time, some cases narrowed excluding shapes. several gains rather than distributions, including for updated collection. The value losses vary collaborative effort's aims, depending needs projection users. Understanding differing methods collect can support accuracy, sustainability, communication infectious disease efforts. All code data available Github: https://github.com/covid19-forecast-hub-europe/aggregation-info-loss

Язык: Английский

Процитировано

4
Projecting the future impact of emerging SARS-CoV-2 variants under uncertainty: Modeling the initial Omicron outbreak DOI Creative Commons
Sean M. Moore, Sean Cavany, T. Alex Perkins

и другие.

Epidemics, Год журнала: 2024, Номер 47, С. 100759 - 100759

Опубликована: Март 2, 2024

Over the past several years, emergence of novel SARS-CoV-2 variants has led to multiple waves increased COVID-19 incidence. When Omicron variant emerged, there was considerable concern about its potential impact in winter 2021-2022 due fitness. However, also uncertainty regarding likely questions relative transmissibility, severity, and degree immune escape. We sought evaluate ability an agent-based model forecast incidence context this emerging pathogen variant. To project cases deaths Indiana, we calibrated our hospitalizations, deaths, test-positivity rates through November 2021, then projected April 2022 under four different scenarios that covered plausible ranges Omicron's Our initial projections from December 2021 March indicated a pessimistic scenario with high disease peak weekly Indiana would be larger than previous 2020. retrospective analyses indicate severity closer optimistic scenario, even though hospitalizations reached new peak, fewer occurred during peak. According results, rapid spread consistent combination higher transmissibility escape earlier variants. updated starting January accurately predicted mid-January decline rapidly over next months. The performance shows following variant, models can help quantify range outbreak magnitudes trajectories. Agent-based are particularly useful these because they efficiently track individual vaccination infection histories varying degrees cross-protection.

Язык: Английский

Процитировано

3
Role of heterogeneity: National scale data-driven agent-based modeling for the US COVID-19 Scenario Modeling Hub DOI Creative Commons
Jiangzhuo Chen, Parantapa Bhattacharya, Stefan Hoops

и другие.

Epidemics, Год журнала: 2024, Номер 48, С. 100779 - 100779

Опубликована: Июнь 28, 2024

UVA-EpiHiper is a national scale agent-based model to support the US COVID-19 Scenario Modeling Hub (SMH). uses detailed representation of underlying social contact network along with data measured during course pandemic initialize and calibrate model. In this paper, we study role heterogeneity on complexity resulting epidemic dynamics using UVA-EpiHiper. We discuss various sources that encounter in use modeling analysis under scenarios. also how affects computational corresponding simulations. Using round 13 SMH as an example, was initialized calibrated. then output produced by can be analyzed obtain interesting insights. find despite model, software, computation incurred scenario modeling, it capable capturing heterogeneities real-world systems, especially those networks behaviors, enables analyzing epidemiological outcomes between different demographic, geographic, cohorts. applying disease are within states, demographic groups, which attributed population demographics, structures, initial immunity.

Язык: Английский

Процитировано

3
Asymmetric limits on timely interventions from noisy epidemic data DOI Creative Commons
Kris V. Parag, Ben Lambert, Christl A. Donnelly

и другие.

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Янв. 28, 2025

Abstract Deciding on when to initiate or relax an intervention in response emerging infectious disease is both difficult and important. Uncertainties from noise epidemiological surveillance data must be hedged against the potentially unknown variable costs of false alarms delayed actions. Here we clarify quantify how case under-reporting latencies ascertainment, which are predominant sources, can restrict timeliness decision-making. Decisions modelled as binary choices between responding not that informed by reported curves transmissibility estimates those curves. Optimal responses triggered thresholds numbers estimate confidence levels, with set various choices. We show that, for growing epidemics, sources induce additive delays hitting any case-based multiplicative reductions our estimated reproduction growth rates. However, declining these have counteracting effects limited cumulative impact estimates. find this asymmetry persists even if more sophisticated feedback control algorithms consider longer-term interventions employed. Standard therefore provide substantially weaker support deciding a action than determining it. This information bottleneck during epidemic may justify proactive

Язык: Английский

Процитировано

0
Evaluation and communication of pandemic scenarios DOI Creative Commons

Philip Gerlee,

Henrik Thorén, Anna Jöud

и другие.

The Lancet Digital Health, Год журнала: 2024, Номер 6(8), С. e543 - e544

Опубликована: Июль 24, 2024

In recent years, publications in The Lancet Digital Health have presented research involving pandemic scenarios.1Nixon K Jindal S Parker F et al.Real-time COVID-19 forecasting: challenges and opportunities of model performance translation.Lancet Health. 2022; 4: e699-e701Summary Full Text PDF PubMed Scopus (6) Google Scholar However, during the early stages pandemic, terms prediction, scenario, forecast were often used interchangeably as discussed by Kristen Nixon colleagues,1Nixon leading to confusion. Although distinctions between these concepts been refined pandemic,2Howerton E Contamin L Mullany LC al.Evaluation US Scenario Modeling Hub for informing response under uncertainty.Nat Commun. 2023; 147260Crossref (16) we find that clarification is needed use scenario projections narrative devices. We also encourage more discussion regarding terminology describe scenarios how they are evaluated. What distinction why important? Underlying all mathematical models that, together with numerical values their parameters (eg, rate transmission), solved numerically generate outputs describing future. These descriptions future states a system usually called predictions. term has denote an unconditional prediction about what will happen future.3Schroeder SA How interpret predictions: reassessing IHME's model.Philosophy Medicine. 2021; 2: 1-7Google By contrast, projection conditional prediction—ie, on set assumptions (ie, scenario). Forecasts typically short (less than month) because uncertainty makes them functionally useless longer time scales, whereas medium long term. separation seen important, difference not clear-cut.4Winsberg Harvard Purposes duties scientific modelling.J Epidemiol Community 76: 512-517Crossref (13) All contain some idealisations need be considered when assessing validity model. One purpose forecasts public health inform expected disease incidence support allocation health-care resources. Such deemed useful many countries especially decision at local or regional levels.5Fox SJ Lachmann M Tec surveillance using hospital admissions mobility data.Proc Natl Acad Sci USA. 119e2111870119Crossref (29) projections, other hand, multitude purposes pandemic—eg, severity outbreaks, estimating effects different vaccination strategies non-pharmaceutical interventions, outlining worst-case bed demand.6Borchering RK Viboud C Howerton al.Modeling cases, hospitalizations, deaths, rates nonpharmaceutical intervention scenarios—United States, April–September 2021.MMWR Morb Mortal Wkly Rep. 70: 719Crossref (106) From health-policy perspective, generated from serve predominately virtual testbeds exploring chains events probable occur given such transmission assumed vaccine roll-out. A taxonomy design was recently put forward based analogy experimental design.7Runge MC Shea al.Scenario infectious projections: integrating analysis design.Epidemics. 2024; 47100775Crossref (1) considering along two independent axes—intervention uncertainty—they identify six classes design, sensitivity analysis, situational awareness, horizon scanning. served another important purpose, namely For example, spring 2020, governments appealed impose social distancing flattening curve predictions projections) backdrops. could, instance, contrast absence admission distancing, observing fell below critical capacity threshold. goes beyond illustrating possible world. Rather, implore presumptive audiences act specific way. this related Runge colleagues' concept 'decision making' does cover persuasive aspect.7Runge Of note, device always clear reported preprints phases could interpreted calls heavier restrictions Gardner colleagues),8Gardner JM Willem Van Der Wijngaart W al.Intervention against estimated impact Swedish healthcare capacity.Int J Epidemiol. 2020; 49: 1443-1453PubMed thus had transactional aim without clearly expressing this. aspects described performativity interactive effects, which refer ability effect world.9Oomen Hoffman Hajer MA Techniques futuring: imagined futures become socially performative.Eur Soc Theory. 25: 252-270Crossref (114) Here, modelers (and makers) responsibility infinite pick handful simulated communicated. choices can control communicated profound contingency unfolds. Given wide range applications modelling, presentation output should align its purpose. Modelers makers explicit underlying and, additionally it results conditioned intended design. Evaluating usefulness complex compared forecasts. Reporting forecasting evaluated comparing actual outcome formal metrics mean absolute percentage error weighted interval score probabilistic forecasts).10Cramer EY Ray EL Lopez VK individual ensemble mortality United States.Proc 119e2113561119Crossref (126) Projections, cannot straight-forwardly outcomes. Formal evaluations performed post-hoc information made real-world framework applied context Hub.2Howerton might difficult implement there no guarantees gathered components build sufficient represent later occurred real if increase post hoc obtain accurate timeframe. Nonetheless, found failed match world still deployed If adherence recommendations improved reduced), then fulfilled although factual far made. same true analyses application precautionary principles policy making. rational scenarios, regards measures endpoints adapted When presenting projection, therefore utmost importance defined unambiguous terminology. communicates estimate virulence agent short-term influence population behaviour reduce spread disease. foundations full, run undermine general trust science institutions. argue strict reporting evaluating help prevent distrust facilitate communication. declare competing interests.

Язык: Английский

Процитировано

0
Preface: COVID-19 Scenario Modeling Hubs DOI Creative Commons
Sara L. Loo, Matteo Chinazzi, Ajitesh Srivastava

и другие.

Epidemics, Год журнала: 2024, Номер 48, С. 100788 - 100788

Опубликована: Авг. 24, 2024

Язык: Английский

Процитировано

0
Design of a Simulation Model for the Diagnosis of Classical Swine Fever Virus in Ecuadorian Farms DOI Open Access

Cristian Inca,

Carlos Bladimir Velasco Moyano, Ángel Isidro Mena Nieto

и другие.

WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE, Год журнала: 2024, Номер 21, С. 345 - 355

Опубликована: Ноя. 5, 2024

Classical swine fever (CSF) is a disease that slows down animal production and international trade; therefore, its identification key in pig farms to take the relevant health measures. Therefore, objective of this research was design Susceptible-Exposed-Infected-Recovered (SEIR) simulation model carry out epidemiological modeling for outbreaks classical Sierra Region Ecuador, using Python software historical data on incidences provinces Ecuadorian highlands, considering variables population, initial number exposed pigs, infected, pigs removed, contagion rate (α), transmission (β), recovery (γ). The results show SEIR allowed us determine population susceptible (healthy) decreases over time until reaching zero. This decrease susceptibility occurred during first 15 days, which shows necessary infect entire with an infected person. increases days total infection process lasts then decreases. It also identified throughout these five years analysis past, it has been increasing from 2015 2019, hurt yields productivity mountains.

Язык: Английский

Процитировано

0