Significance of supervision sampling in control of communicable respiratory disease simulated by a new model during different stages of the disease
Abstract The coronavirus disease 2019 (COVID-19) interventions in interrupting transmission have paid heavy losses politically and economically. The Chinese government has replaced scaling up testing with monitoring focus groups and randomly supervising sampling, encouraging scientific research on t...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-025-86739-9 |
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| author | Alphonse Houssou Hounye Xiaogao Pan Yuqi Zhao Cong Cao Jiaoju Wang Abidi Mimi Venunye Li Xiong Xiangping Chai Muzhou Hou |
| author_facet | Alphonse Houssou Hounye Xiaogao Pan Yuqi Zhao Cong Cao Jiaoju Wang Abidi Mimi Venunye Li Xiong Xiangping Chai Muzhou Hou |
| author_sort | Alphonse Houssou Hounye |
| collection | DOAJ |
| description | Abstract The coronavirus disease 2019 (COVID-19) interventions in interrupting transmission have paid heavy losses politically and economically. The Chinese government has replaced scaling up testing with monitoring focus groups and randomly supervising sampling, encouraging scientific research on the COVID-19 transmission curve to be confirmed by constructing epidemiological models, which include statistical models, computer simulations, mathematical illustrations of the pathogen and its effects, and several other methodologies. Although predicting and forecasting the propagation of COVID-19 are valuable, they nevertheless present an enormous challenge. This paper emphasis on pandemic simulation models by introduced respiratory-specific transmission to extend and complement the classical Susceptible-Exposed-(Asymptomatic)-Infected-Recovered SE(A)IR model to assess the significance of the COVID-19 transmission control features to provide an explanation of the rationale for the government policy. A novel epidemiological model is developed using mean-field theory. Utilizing the SE(A)IR extended framework, which is a suitable method for describing the progression of epidemics over actual or genuine landscapes, we have developed a novel model named SEIAPUFR. This model effectively detects the connections between various stages of infection. Subsequently, we formulated eight ordinary differential equations that precisely depict the population’s temporal development inside each segment. Furthermore, we calibrated the transmission and clearance rates by considering the impact of various control strategies on the epidemiological dynamics, which we used to project the future course of COVID-19. Based on these parameter values, our emphasis was on determining the criteria for stabilizing the disease-free equilibrium (DEF). We also developed model parameters that are appropriate for COVID-19 outbreaks, taking into account varied population sizes. Ultimately, we conducted simulations and predictions for other prominent cities in China, such as Wuhan, Shanghai, Guangzhou, and Shenzhen, that have recently been affected by the COVID-19 outbreak. By integrating different control measures, respiratory-specific modeling, and disease supervision sampling into an expanded SEI (A) R epidemic model, we found that supervision sampling can improve early warning of viral activity levels and superspreading events, and explained the significance of containments in controlling COVID-19 transmission and the rationality of policy by the influence of different containment measures on the transmission rate. These results indicate that the control measures during the pandemic interrupted the transmission chain mainly by inhibiting respiratory transmission, and the proportion of supervision sampling should be proportional to the transmission rate, especially only aimed at preventing a resurgence of SARS-CoV-2 transmission in low-prevalence areas. Furthermore, The incidence hazard of Males and Females was 1.39(1.23–1.58), and 1.43(1.26–1.63), respectively. Our investigation found that the ratio of peak sampling is directly related to the transmission rate, and both decrease when control measures are implemented. Consequently, the control measures during the pandemic interrupted the transmission chain mainly by inhibiting respiratory transmission. Reasonable and effective interventions during the early stage can flatten the transmission curve, which will slow the momentum of the outbreak to reduce medical pressure. |
| format | Article |
| id | doaj-art-715fd7986a354b65b0007f8735738fe5 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-715fd7986a354b65b0007f8735738fe52025-02-02T12:19:01ZengNature PortfolioScientific Reports2045-23222025-01-0115112910.1038/s41598-025-86739-9Significance of supervision sampling in control of communicable respiratory disease simulated by a new model during different stages of the diseaseAlphonse Houssou Hounye0Xiaogao Pan1Yuqi Zhao2Cong Cao3Jiaoju Wang4Abidi Mimi Venunye5Li Xiong6Xiangping Chai7Muzhou Hou8General Surgery Department of Second Xiangya Hospital, Central South University ChangshaDepartment of Emergency Medicine, Second Xiangya Hospital, Central South UniversityDepartment of Gastroenterology, The Second Xiangya Hospital, Central South UniversitySchool of Mathematics and Statistics, Central South UniversitySchool of Mathematics and Statistics, Central South UniversityGeneral Surgery Department of Second Xiangya Hospital, Central South University ChangshaGeneral Surgery Department of Second Xiangya Hospital, Central South University ChangshaDepartment of Emergency Medicine, Second Xiangya Hospital, Central South UniversitySchool of Mathematics and Statistics, Central South UniversityAbstract The coronavirus disease 2019 (COVID-19) interventions in interrupting transmission have paid heavy losses politically and economically. The Chinese government has replaced scaling up testing with monitoring focus groups and randomly supervising sampling, encouraging scientific research on the COVID-19 transmission curve to be confirmed by constructing epidemiological models, which include statistical models, computer simulations, mathematical illustrations of the pathogen and its effects, and several other methodologies. Although predicting and forecasting the propagation of COVID-19 are valuable, they nevertheless present an enormous challenge. This paper emphasis on pandemic simulation models by introduced respiratory-specific transmission to extend and complement the classical Susceptible-Exposed-(Asymptomatic)-Infected-Recovered SE(A)IR model to assess the significance of the COVID-19 transmission control features to provide an explanation of the rationale for the government policy. A novel epidemiological model is developed using mean-field theory. Utilizing the SE(A)IR extended framework, which is a suitable method for describing the progression of epidemics over actual or genuine landscapes, we have developed a novel model named SEIAPUFR. This model effectively detects the connections between various stages of infection. Subsequently, we formulated eight ordinary differential equations that precisely depict the population’s temporal development inside each segment. Furthermore, we calibrated the transmission and clearance rates by considering the impact of various control strategies on the epidemiological dynamics, which we used to project the future course of COVID-19. Based on these parameter values, our emphasis was on determining the criteria for stabilizing the disease-free equilibrium (DEF). We also developed model parameters that are appropriate for COVID-19 outbreaks, taking into account varied population sizes. Ultimately, we conducted simulations and predictions for other prominent cities in China, such as Wuhan, Shanghai, Guangzhou, and Shenzhen, that have recently been affected by the COVID-19 outbreak. By integrating different control measures, respiratory-specific modeling, and disease supervision sampling into an expanded SEI (A) R epidemic model, we found that supervision sampling can improve early warning of viral activity levels and superspreading events, and explained the significance of containments in controlling COVID-19 transmission and the rationality of policy by the influence of different containment measures on the transmission rate. These results indicate that the control measures during the pandemic interrupted the transmission chain mainly by inhibiting respiratory transmission, and the proportion of supervision sampling should be proportional to the transmission rate, especially only aimed at preventing a resurgence of SARS-CoV-2 transmission in low-prevalence areas. Furthermore, The incidence hazard of Males and Females was 1.39(1.23–1.58), and 1.43(1.26–1.63), respectively. Our investigation found that the ratio of peak sampling is directly related to the transmission rate, and both decrease when control measures are implemented. Consequently, the control measures during the pandemic interrupted the transmission chain mainly by inhibiting respiratory transmission. Reasonable and effective interventions during the early stage can flatten the transmission curve, which will slow the momentum of the outbreak to reduce medical pressure.https://doi.org/10.1038/s41598-025-86739-9SARS-CoV-2 transmissionDisease-free equilibriumTransmission rateSEI(A)R epidemic model |
| spellingShingle | Alphonse Houssou Hounye Xiaogao Pan Yuqi Zhao Cong Cao Jiaoju Wang Abidi Mimi Venunye Li Xiong Xiangping Chai Muzhou Hou Significance of supervision sampling in control of communicable respiratory disease simulated by a new model during different stages of the disease Scientific Reports SARS-CoV-2 transmission Disease-free equilibrium Transmission rate SEI(A)R epidemic model |
| title | Significance of supervision sampling in control of communicable respiratory disease simulated by a new model during different stages of the disease |
| title_full | Significance of supervision sampling in control of communicable respiratory disease simulated by a new model during different stages of the disease |
| title_fullStr | Significance of supervision sampling in control of communicable respiratory disease simulated by a new model during different stages of the disease |
| title_full_unstemmed | Significance of supervision sampling in control of communicable respiratory disease simulated by a new model during different stages of the disease |
| title_short | Significance of supervision sampling in control of communicable respiratory disease simulated by a new model during different stages of the disease |
| title_sort | significance of supervision sampling in control of communicable respiratory disease simulated by a new model during different stages of the disease |
| topic | SARS-CoV-2 transmission Disease-free equilibrium Transmission rate SEI(A)R epidemic model |
| url | https://doi.org/10.1038/s41598-025-86739-9 |
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