Modeling the Spread of Infectious Diseases: A Review

Chen, Dongmei

doi:10.1002/9781118630013.ch2

Cited by 31 publications

(18 citation statements)

References 129 publications

(159 reference statements)

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“…As the viral generations of positive samples are unknown we modeled the containment in Iceland in an epidemiological model that tracks the viral generation of positive samples in Iceland and incorporates the observed molecular traces of SARS-CoV-2 infection. To model this prolonged detection of SARS-COV-2 viral RNA, we considered an extension of the susceptible, infected, and recovered (SIR) epidemiological model, which allows modeling of the spread of a disease in a closed population 16 . We extended this model to record the number of viral generations and included a state (SIPR model, Fig.…”

Section: Resultsmentioning

confidence: 99%

Molecular benchmarks of a SARS-CoV-2 epidemic

et al. 2021

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A pressing concern in the SARS-CoV-2 epidemic and other viral outbreaks, is the extent to which the containment measures are halting the viral spread. A straightforward way to assess this is to tally the active cases and the recovered ones throughout the epidemic. Here, we show how epidemic control can be assessed with molecular information during a well characterized epidemic in Iceland. We demonstrate how the viral concentration decreased in those newly diagnosed as the epidemic transitioned from exponential growth phase to containment phase. The viral concentration in the cases identified in population screening decreased faster than in those symptomatic and considered at high risk and that were targeted by the healthcare system. The viral concentration persists in recovering individuals as we found that half of the cases are still positive after two weeks. We demonstrate that accumulation of mutations in SARS-CoV-2 genome can be exploited to track the rate of new viral generations throughout the different phases of the epidemic, where the accumulation of mutations decreases as the transmission rate decreases in the containment phase. Overall, the molecular signatures of SARS-CoV-2 infections contain valuable epidemiological information that can be used to assess the effectiveness of containment measures.

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Section: Resultsmentioning

confidence: 99%

Molecular benchmarks of a SARS-CoV-2 epidemic

et al. 2021

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“…We also include population density as an external feature for further analysis. We then integrate the determinant factors with disease incidence rate at each hotspot location and find the distance among these locations based on Equations (10) and (11), followed by the summation of the correlation matrices over the weekly range. Figure 8 gives the heat map for distances among the hotspots based on all the candidate factors.…”

Section: Resultsmentioning

confidence: 99%

“…Mathematical models to represent diseases date back to 1909 and are still widely used to represent disease transmission [11][12][13][14]. Mathematical modeling represents elements of disease transmission dynamics and other candidate factors of interest.…”

Section: Related Workmentioning

confidence: 99%

An Integrated Approach for Spatio-Temporal Cholera Disease Hotspot Relation Mining for Public Health Management in Punjab, Pakistan

Khalique

Khan

Butt

et al. 2020

IJERPH

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Public health management can generate actionable results when diseases are studied in context with other candidate factors contributing to disease dynamics. In order to fully understand the interdependent relationships of multiple geospatial features involved in disease dynamics, it is important to construct an effective representation model that is able to reveal the relationship patterns and trends. The purpose of this work is to combine disease incidence spatio-temporal data with other features of interest in a mutlivariate spatio-temporal model for investigating characteristic disease and feature patterns over identified hotspots. We present an integrated approach in the form of a disease management model for analyzing spatio-temporal dynamics of disease in connection with other determinants. Our approach aligns spatio-temporal profiles of disease with other driving factors in public health context to identify hotspots and patterns of disease and features of interest in the identified locations. We evaluate our model against cholera disease outbreaks from 2015–2019 in Punjab province of Pakistan. The experimental results showed that the presented model effectively address the complex dynamics of disease incidences in the presence of other features of interest over a geographic area representing populations and sub populations during a given time. The presented methodology provides an effective mechanism for identifying disease hotspots in multiple dimensions and relation between the hotspots for cost-effective and optimal resource allocation as well as a sound reference for further predictive and forecasting analysis.

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“…To demonstrate the possible outcome of a pandemic and warn public health interventions, mathematical models may project how infectious diseases spread. Moreover, to determine parameters for different infectious diseases, models employ simple assumptions or gathered statistics along with mathematics to measure the results of various interventions (2). Modeling can determine which intervention(s) may prevent, test, or forecast potential development trends (3).…”

Section: Introductionmentioning

confidence: 99%

Future of Mathematical Modelling: A Review of COVID-19 Infected Cases Using S-I-R Model

Yunus

Ibrahim

et al. 2021

Baghdad Sci.J

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The spread of novel coronavirus disease (COVID-19) has resulted in chaos around the globe. The infected cases are still increasing, with many countries still showing a trend of growing daily cases. To forecast the trend of active cases, a mathematical model, namely the SIR model was used, to visualize the spread of COVID-19. For this article, the forecast of the spread of the virus in Malaysia has been made, assuming that all Malaysian will eventually be susceptible. With no vaccine and antiviral drug currently developed, the visualization of how the peak of infection (namely flattening the curve) can be reduced to minimize the effect of COVID-19 disease. For Malaysians, let's ensure to follow the rules and obey the SOP to lower the R 0 value from time to time, hoping that the virus will vanish one day.

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Modeling the Spread of Infectious Diseases: A Review

Cited by 31 publications

References 129 publications

Molecular benchmarks of a SARS-CoV-2 epidemic

Molecular benchmarks of a SARS-CoV-2 epidemic

An Integrated Approach for Spatio-Temporal Cholera Disease Hotspot Relation Mining for Public Health Management in Punjab, Pakistan

Future of Mathematical Modelling: A Review of COVID-19 Infected Cases Using S-I-R Model

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