# Estimating Asymptomatic and Symptomatic Transmission of the COVID‐19 First Few Cases in Selenge Province, Mongolia

## TL;DR

This study estimates how much asymptomatic and symptomatic people contributed to spreading COVID-19 in a Mongolian province, emphasizing the need to isolate symptomatic cases early.

## Contribution

The study introduces a novel method combining contact tracing and statistical modeling to estimate transmission rates for different symptom statuses.

## Key findings

- Most cases were transmitted within households or workplaces.
- Symptomatic cases had a higher reproduction number than asymptomatic or presymptomatic cases.
- The approach can be applied to other infectious diseases for better transmission understanding.

## Abstract

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.

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.

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 R0Asy and the presymptomatic cases R0Pre (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]).

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.