Tweet
Influenza Other Respir Viruses
. 2024 Apr;18(4):e13277.
doi: 10.1111/irv.13277. Estimating Asymptomatic and Symptomatic Transmission of the COVID-19 First Few Cases in Selenge Province, Mongolia
Davaalkham Dambadarjaa 1 , Tsogt Mend 2 , Andrew Shapiro 3 , Mark S Handcock 3 , Undram Mandakh 4 , Temuulen Enebish 5 , Linh-Vi Le 5 , Dj Darwin R Bandoy 5 6 , Ambaselmaa Amarjargal 2 , Bilegt Altangerel 2 , Tuvshintur Chuluunbaatar 2 , Uugantsetseg Guruuchin 2 , Oyuntulkhuur Lkhagvajav 2 , Oyunsuren Enebish 7
Affiliations
Background: Following the first locally transmitted case in Sukhbaatar soum, Selenge Province, we aimed to investigate the ultimate scale of the epidemic in the scenario of uninterrupted transmission.
Methods: This was a prospective case study following the locally modified WHO FFX cases generic protocol. A rapid response team collected data from November 14 to 29, 2020. We created a stochastic process to draw many transmission chains from this greater distribution to better understand and make inferences regarding the outbreak under investigation.
Results: The majority of the cases involved household transmissions (35, 52.2%), work transmissions (20, 29.9%), index (5, 7.5%), same apartment transmissions (2, 3.0%), school transmissions (2, 3.0%), and random contacts between individuals transmissions (1, 1.5%). The posterior means of the basic reproduction number of both the asymptomatic cases RAsy0
and the presymptomatic cases RPre0 (1.35 [95% CrI 0.88-1.86] and 1.29 [95% CrI 0.67-2.10], respectively) were lower than that of the symptomatic cases (2.00 [95% Crl 1.38-2.76]).
Conclusion: Our study highlights the heterogeneity of COVID-19 transmission across different symptom statuses and underscores the importance of early identification and isolation of symptomatic cases in disease control. Our approach, which combines detailed contact tracing data with advanced statistical methods, can be applied to other infectious diseases, facilitating a more nuanced understanding of disease transmission dynamics.
Keywords: Bayesian statistics; COVID‐19*/epidemiology; SARS‐CoV‐2; coronavirus infections/epidemiology*; family characteristics; field epidemiology; human*; influenza; pandemics*.
. 2024 Apr;18(4):e13277.
doi: 10.1111/irv.13277. Estimating Asymptomatic and Symptomatic Transmission of the COVID-19 First Few Cases in Selenge Province, Mongolia
Davaalkham Dambadarjaa 1 , Tsogt Mend 2 , Andrew Shapiro 3 , Mark S Handcock 3 , Undram Mandakh 4 , Temuulen Enebish 5 , Linh-Vi Le 5 , Dj Darwin R Bandoy 5 6 , Ambaselmaa Amarjargal 2 , Bilegt Altangerel 2 , Tuvshintur Chuluunbaatar 2 , Uugantsetseg Guruuchin 2 , Oyuntulkhuur Lkhagvajav 2 , Oyunsuren Enebish 7
Affiliations
- PMID: 38544454
- DOI: 10.1111/irv.13277
Background: Following the first locally transmitted case in Sukhbaatar soum, Selenge Province, we aimed to investigate the ultimate scale of the epidemic in the scenario of uninterrupted transmission.
Methods: This was a prospective case study following the locally modified WHO FFX cases generic protocol. A rapid response team collected data from November 14 to 29, 2020. We created a stochastic process to draw many transmission chains from this greater distribution to better understand and make inferences regarding the outbreak under investigation.
Results: The majority of the cases involved household transmissions (35, 52.2%), work transmissions (20, 29.9%), index (5, 7.5%), same apartment transmissions (2, 3.0%), school transmissions (2, 3.0%), and random contacts between individuals transmissions (1, 1.5%). The posterior means of the basic reproduction number of both the asymptomatic cases RAsy0
and the presymptomatic cases RPre0 (1.35 [95% CrI 0.88-1.86] and 1.29 [95% CrI 0.67-2.10], respectively) were lower than that of the symptomatic cases (2.00 [95% Crl 1.38-2.76]).
Conclusion: Our study highlights the heterogeneity of COVID-19 transmission across different symptom statuses and underscores the importance of early identification and isolation of symptomatic cases in disease control. Our approach, which combines detailed contact tracing data with advanced statistical methods, can be applied to other infectious diseases, facilitating a more nuanced understanding of disease transmission dynamics.
Keywords: Bayesian statistics; COVID‐19*/epidemiology; SARS‐CoV‐2; coronavirus infections/epidemiology*; family characteristics; field epidemiology; human*; influenza; pandemics*.