Tooling-up for infectious disease transmission modelling

Baguelin, Marc; Medley, Graham F.; Nightingale, Emily; O’Reilly, Kathleen; Rees, Eleanor M.; Waterlow, Naomi R.; Wagner, Moritz

doi:10.1016/j.epidem.2020.100395

Cited by 11 publications

(8 citation statements)

References 42 publications

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“…Although it provides a simple expression for model calibration, it makes the assumption that transmission is stable over the years, and that transmission would remain at the same constant level in the absence of any intervention changes. If sufficient data is available to study the temporal trend in reported incidence, other statistical methods for fitting [43] could be used to relax this assumption. Moreover, as countries get closer to elimination and reach very low incidence levels, the steady-state assumption is unlikely to hold at local levels and other models could be used, either by considering connectivity across all areas and assuming global equilibrium only [40] , [44] or by using other approaches for foci detection in very low transmission settings (less than 20 reported cases per year) similar to [45] .…”

Section: Discussionmentioning

confidence: 99%

Using observed incidence to calibrate the transmission level of a mathematical model for Plasmodium vivax dynamics including case management and importation

Champagne

Gerhards

Lana

et al. 2022

Mathematical Biosciences

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Section: Discussionmentioning

confidence: 99%

Using observed incidence to calibrate the transmission level of a mathematical model for Plasmodium vivax dynamics including case management and importation

Champagne

Gerhards

Lana

et al. 2022

Mathematical Biosciences

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“…There is obvious heterogeneity in contact among individuals [18] , and IDD model based on network control measures is of great significance in the scenario prediction of public health events. The final scale of epidemics and epidemic time are two important dynamic indicators for evaluating the severity of infectious diseases [19] .…”

Section: Methodsmentioning

confidence: 99%

Scenario prediction of public health emergencies using infectious disease dynamics model and dynamic Bayes

Gao

Wang

2022

Future Generation Computer Systems

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This study was aimed to discuss the predictive value of infectious disease dynamics model (IDD model) and dynamic Bayesian network (DBN) for scenario deduction of public health emergencies (PHEs). Based on the evolution law of PHEs and the meta-scenario representation of basic knowledge, this study established a DBN scenario deduction model for scenario deduction and evolution path analysis of PHEs. At the same time, based on the average field dynamics model of the SIR network, the dimensionality reduction process was performed to calculate the epidemic scale and epidemic time based on the IDD model, so as to determine the calculation methods of threshold value and epidemic time under emergency measures (quarantine). The Corona Virus Disease (COVID) epidemic was undertaken as an example to analyze the results of DBN scenario deduction, and the infectious disease dynamics model was used to analyze the number of reproductive numbers, peak arrival time, epidemic time, and latency time of the COVID epidemic. It was found that after the M1 measure was used to process the S1 state, the state probability and the probability of being true (T) were the highest, which were 91.05 and 90.21, respectively. In the sixth stage of the development of the epidemic, the epidemic had developed to level 5, the number of infected people was about 26, and the estimated loss was about 220 million yuan. The comprehensive cumulative foreground (CF) values of O1 O3 schemes were -1.34, -1.21, and -0.77, respectively, and the final CF values were -1.35, 0.01, and -0.08, respectively. The final CF value of O2 was significantly higher than the other two options. The household infection probability was the highest, which was 0.37 and 0.35 in Wuhan and China, respectively. Under the measures of home quarantine, the numbers of confirmed cases of COVID in China and Wuhan were 1.503 (95% confidential interval (CI) = 1.328 1.518) and 1.729 (95% CI = 1.107 1.264), respectively, showing good fits with the real data. On the 21st day after the quarantine measures were taken, the number of COVID across the country had an obvious peak, with the confirmed cases of 24495, and the model prediction value was 24085 (95% CI = 23988 25056). The incubation period 1/q was shortened from 8 days to 3 days, and the number of confirmed cases showed an upward trend. The peak period of confirmed cases was advanced, shortening the overall epidemic time. It showed that the prediction results of scenario deduction based on DBN were basically consistent with the actual development scenario and development status of the epidemic. It could provide corresponding decisions for the prevention and control of COVID based on the relevant parameters of the infectious disease dynamic model, which verified the rationality and feasibility of the scenario deduction method proposed in this study.

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“…Epidemic dynamics is a mathematical model established by nonlinear dynamics. According to the general dissemination mechanism of epidemics, the dissemination process of epidemics is described through quantitative relationships, the change law of the number of infected individuals is analyzed, and the development pattern of epidemics is revealed [43]. The most basic SI model divides the population into two types: Susceptible and infected.…”

Section: Clpp Dissemination Modelmentioning

confidence: 99%

Modeling and Simulation of Dissemination of Cultivated Land Protection Policies in China

Zhang

Tang

2021

Land

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Cultivated land protection is the top priority of the national economy in China and the livelihood of people. Cultivated land protection policies (CLPP) play an important role in the protection of cultivated land. However, the process of dissemination of CLPP on social networks of farmers has problems, such as distortion of policy content, single dissemination channels, low level of farmers’ knowledge, and low dissemination efficiency. For revealing the characteristics of the dissemination of CLPP in the farmers’ social networks (FSN), this study combines the Suspected–Exposed–Infected–Recovered–Suspected (SEIRS) epidemic model to construct a model of CLPP dissemination suitable for FSN. In addition, a numerical simulation of the dissemination process of CLPP is conducted on the FSN, and the influence of the structural characteristics of the FSN and different model parameters on the dissemination of CLPP is analyzed. Results show that (1) the dissemination rate between farmers in FSN has a significant impact on the scale and speed of CLPP. A greater initial dissemination rate corresponds to faster speed and larger scale of CLPP dissemination. (2) A greater node degree in FSN means stronger dissemination ability for CLPP. Therefore, identifying structural holes (opinion leaders) in FSN can effectively promote the dissemination of CLPP. (3) The SEIRS model can dynamically describe the evolution law of CLPP dissemination process over time through the four states of farmer nodes of suspected, exposed, infected, and recovered. Numerical simulation results show that the immune degradation rate is proportional to CLPP. However, the direct immunization rate is inversely proportional. The increase in immune degradation rate can reduce the number of recovered farmers and improve the efficiency of CLPP dissemination. On the basis of the abovementioned conclusions, this study draws policy recommendations to increase the scale and speed of CLPP dissemination in China.

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Tooling-up for infectious disease transmission modelling

Cited by 11 publications

References 42 publications

Using observed incidence to calibrate the transmission level of a mathematical model for Plasmodium vivax dynamics including case management and importation

Using observed incidence to calibrate the transmission level of a mathematical model for Plasmodium vivax dynamics including case management and importation

Scenario prediction of public health emergencies using infectious disease dynamics model and dynamic Bayes

Modeling and Simulation of Dissemination of Cultivated Land Protection Policies in China

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