scholarly journals Effects of control measures and their impacts on COVID-19 transmission dynamics

2021 ◽  
Author(s):  
Chonawee Supatgiat
Keyword(s):  
Markov Chain Model ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Contact Tracing ◽  
Chain Model ◽  
School Closures ◽  
Public Places ◽  
Discrete Time Markov Chain ◽  
Changes Over Time ◽  
Testing Protocols

Abstract Governments around the world have grappled with the COVID-19 pandemic for more than a year. Control measures such as social distancing, use of face masks in public places, business and school closures, city or transportation lockdowns, mass gathering bans, population education and engagement, contact tracing, and improved mass testing protocols are being used to contain the pandemic. Currently, there are no studies to date that rank the effectiveness of these measures, resulting in government responses that may be uncoordinated and inefficient. In this study, we developed a Discrete Time Markov Chain model that captures the above control measures and ranks them. We found that the importance of the measures changes over time and depends on the stage of transmission dynamics, as well as the ecological environment. For example, contact tracing is a powerful measure to effectively control the pandemic, however, our results show that while it is indeed helpful during the early stages of the pandemic, it is much less important after a vaccination program takes effect. Besides, our model improved the standard SEIR compartmental model by taking into account the dynamic temporal transmission and recovery probabilities along with considering a portion of the population that will not comply with government-mandated control measures. If implemented, our novel and unique model may assist many countries in pandemic control decisions.

scholarly journals Effects of control measures and their importance on COVID-19 transmission dynamics

2021 ◽  
Author(s):  
Chonawee Supatgiat
Keyword(s):  
Early Stage ◽  
Markov Chain Model ◽  
Face Mask ◽  
School Closure ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Contact Tracing ◽  
Effective Control ◽  
Chain Model ◽  
Testing Protocols

Abstract For more than a year, governments around the world have attempted to control the COVID-19 pandemic. Control measures such as social distancing, face mask wearing, business/school closure, city or transportation lockdown, ban of mass gathering, population education and engagement, contact tracing, and improved mass testing protocols are being used to contain the pandemic. Currently, there are no studies to date that rank the importance of these measures so that the governments may allocate and target their resources towards the most effective control measures. In this paper, we propose a Discrete Time Markov Chain model that captures the above control measures and ranks them. We also show that the importance of the measures change overtime and depends on the stage of the transmission dynamics, as well as the environment. For example, contract tracing is known to be a powerful measure to effectively control the pandemic, however its influence is dynamic in nature. Our results show that contact tracing is indeed helpful during the early stage of the pandemic, but becomes less important after a vaccination program takes effect. If implemented, our novel and unique model may assist many countries in their crucial pandemic control decisions.

Predictive Modeling for Epidemic Outbreaks: A New Approach and COVID-19 Case Study

2020 ◽  
Vol 37 (03) ◽  
pp. 2050028
Author(s):  
Jian Chen ◽  
Michael C. Fu ◽  
Wenhong Zhang ◽  
Junhua Zheng
Keyword(s):  
Markov Chain Model ◽  
Capital City ◽  
Chain Model ◽  
New Approach ◽  
Discrete Time Markov Chain ◽  
The World ◽  
Entire Country ◽  
Provincial Capital ◽  
Coronavirus Infection

Since the onset of the COVID-19 outbreak in Wuhan, China, numerous forecasting models have been proposed to project the trajectory of coronavirus infection cases. Most of these forecasts are based on epidemiology models that utilize deterministic differential equations and have resulted in widely varying predictions. We propose a new discrete-time Markov chain model that directly incorporates stochastic behavior and for which parameter estimation is straightforward from available data. Using such data from China’s Hubei province (for which Wuhan is the provincial capital city and which accounted for approximately 82% of the total reported COVID-19 cases in the entire country), the model is shown to be flexible, robust, and accurate. As a result, it has been adopted by the first Shanghai assistance medical team in Wuhan’s Jinyintan Hospital, which was the first designated hospital to take COVID-19 patients in the world. The forecast has been used for preparing medical staff, intensive care unit (ICU) beds, ventilators, and other critical care medical resources and for supporting real-time medical management decisions.

scholarly journals Using a stochastic continuous-time Markov chain model to examine alternative timing and duration of the COVID-19 lockdown in Kuwait: what can be done now?

2022 ◽  
Vol 80 (1) ◽  
Author(s):  
Mustafa Al-Zoughool ◽  
Tamer Oraby ◽  
Harri Vainio ◽  
Janvier Gasana ◽  
Joseph Longenecker ◽  
...  
Keyword(s):  
Markov Chain ◽  
Continuous Time ◽  
Disease Transmission ◽  
Markov Chain Model ◽  
Substantial Reduction ◽  
Control Measures ◽  
Optimal Timing ◽  
Chain Model ◽  
Continuous Time Markov Chain ◽  
Epidemic Curve

Abstract Background Kuwait had its first COVID-19 in late February, and until October 6, 2020 it recorded 108,268 cases and 632 deaths. Despite implementing one of the strictest control measures-including a three-week complete lockdown, there was no sign of a declining epidemic curve. The objective of the current analyses is to determine, hypothetically, the optimal timing and duration of a full lockdown in Kuwait that would result in controlling new infections and lead to a substantial reduction in case hospitalizations. Methods The analysis was conducted using a stochastic Continuous-Time Markov Chain (CTMC), eight state model that depicts the disease transmission and spread of SARS-CoV 2. Transmission of infection occurs between individuals through social contacts at home, in schools, at work, and during other communal activities. Results The model shows that a lockdown 10 days before the epidemic peak for 90 days is optimal but a more realistic duration of 45 days can achieve about a 45% reduction in both new infections and case hospitalizations. Conclusions In the view of the forthcoming waves of the COVID19 pandemic anticipated in Kuwait using a correctly-timed and sufficiently long lockdown represents a workable management strategy that encompasses the most stringent form of social distancing with the ability to significantly reduce transmissions and hospitalizations.

scholarly journals Estimating transmission dynamics and serial interval of the first wave of COVID-19 infections under different control measures: A statistical analysis in Tunisia from February 29 to May 5, 2020

2020 ◽  
Author(s):  
Khouloud Talmoudi ◽  
Mouna Safer ◽  
Hejer Letaief ◽  
Aicha Hchaichi ◽  
Chahida Harizi ◽  
...  
Keyword(s):  
Emerging Diseases ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Contact Tracing ◽  
Epidemic Threshold ◽  
Illness Onset ◽  
Intervention Measures ◽  
Temporal Reproduction ◽  
Prevention Measure ◽  
Serial Interval

Abstract Background Describing transmission dynamics of the outbreak and impact of intervention measures are critical to planning responses to future outbreaks and providing timely information to guide policy makers decision. We estimate serial interval (SI) and temporal reproduction number (Rt) of SARS-CoV-2 in Tunisia. Methods We collected data of investigations and contact tracing between March 1, 2020 and May 5, 2020 as well as illness onset data during the period February 29-May 5, 2020 from National Observatory of New and Emerging Diseases of Tunisia. Maximum likelihood (ML) approach is used to estimate dynamics of Rt. Results 491 of infector-infectee pairs were involved, with 14.46% reported pre-symptomatic transmission. SI follows Gamma distribution with mean 5.30 days [95% CI 4.66–5.95] and standard deviation 0.26 [95% CI 0.23–0.30]. Also, we estimated large changes in Rt in response to the combined lockdown interventions. The Rt moves from 3.18 [95% CI 2.73–3.69] to 1.77 [95% CI 1.49–2.08] with curfew prevention measure, and under the epidemic threshold (0.89 [95% CI 0.84–0.94]) by national lockdown measure. Conclusions Overall, our findings highlight contribution of interventions to interrupt transmission of SARS-CoV-2 in Tunisia.

scholarly journals Estimating transmission dynamics and serial interval of the first wave of COVID-19 infections under different control measures: a statistical analysis in Tunisia from February 29 to May 5, 2020

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Khouloud Talmoudi ◽  
Mouna Safer ◽  
Hejer Letaief ◽  
Aicha Hchaichi ◽  
Chahida Harizi ◽  
...  
Keyword(s):  
Credible Interval ◽  
Emerging Diseases ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Contact Tracing ◽  
Epidemic Threshold ◽  
Illness Onset ◽  
Intervention Measures ◽  
Temporal Reproduction ◽  
Serial Interval

Abstract Background Describing transmission dynamics of the outbreak and impact of intervention measures are critical to planning responses to future outbreaks and providing timely information to guide policy makers decision. We estimate serial interval (SI) and temporal reproduction number (Rt) of SARS-CoV-2 in Tunisia. Methods We collected data of investigations and contact tracing between March 1, 2020 and May 5, 2020 as well as illness onset data during the period February 29–May 5, 2020 from National Observatory of New and Emerging Diseases of Tunisia. Maximum likelihood (ML) approach is used to estimate dynamics of Rt. Results Four hundred ninety-one of infector-infectee pairs were involved, with 14.46% reported pre-symptomatic transmission. SI follows Gamma distribution with mean 5.30 days [95% Confidence Interval (CI) 4.66–5.95] and standard deviation 0.26 [95% CI 0.23–0.30]. Also, we estimated large changes in Rt in response to the combined lockdown interventions. The Rt moves from 3.18 [95% Credible Interval (CrI) 2.73–3.69] to 1.77 [95% CrI 1.49–2.08] with curfew prevention measure, and under the epidemic threshold (0.89 [95% CrI 0.84–0.94]) by national lockdown measure. Conclusions Overall, our findings highlight contribution of interventions to interrupt transmission of SARS-CoV-2 in Tunisia.

scholarly journals Markov Chain Modeling of HIV, Tuberculosis, and Hepatitis B Transmission in Ghana

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Clement Twumasi ◽  
Louis Asiedu ◽  
Ezekiel N. N. Nortey
Keyword(s):  
Markov Chain ◽  
Hepatitis B ◽  
Discrete Time ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Chain Model ◽  
Disease Dynamics ◽  
Epidemiological Models ◽  
Discrete Time Markov Chain ◽  
Expected Time

Several mathematical and standard epidemiological models have been proposed in studying infectious disease dynamics. These models help to understand the spread of disease infections. However, most of these models are not able to estimate other relevant disease metrics such as probability of first infection and recovery as well as the expected time to infection and recovery for both susceptible and infected individuals. That is, most of the standard epidemiological models used in estimating transition probabilities (TPs) are not able to generalize the transition estimates of disease outcomes at discrete time steps for future predictions. This paper seeks to address the aforementioned problems through a discrete-time Markov chain model. Secondary datasets from cohort studies were collected on HIV, tuberculosis (TB), and hepatitis B (HB) cases from a regional hospital in Ghana. The Markov chain model revealed that hepatitis B was more infectious over time than tuberculosis and HIV even though the probability of first infection of these diseases was relatively low within the study population. However, individuals infected with HIV had comparatively lower life expectancies than those infected with tuberculosis and hepatitis B. Discrete-time Markov chain technique is recommended as viable for modeling disease dynamics in Ghana.

Probability of maintaining or attaining a structure in one step

1987 ◽  
Vol 24 (4) ◽  
pp. 1006-1011 ◽  
Author(s):  
G. Abdallaoui
Keyword(s):  
Markov Chain ◽  
Discrete Time ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Chain Model ◽  
Discrete Time Markov Chain ◽  
One Step ◽  
Manpower System

Our concern is with a particular problem which arises in connection with a discrete-time Markov chain model for a graded manpower system. In this model, the members of an organisation are classified into distinct classes. As time passes, they move from one class to another, or to the outside world, in a random way governed by fixed transition probabilities. In this paper, the emphasis is placed on evaluating exact values of the probabilities of attaining and maintaining a structure.

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