A study in Guangzhou, China, provides key insights into how COVID-19 spreads at home

Modeling research published in Lancet infection The journal says that the coronavirus that causes COVID-19 (SARS-CoV-2) could easily spread to people and families who live with it more than with Severe Acute Respiratory Syndrome (SARS) or Middle East Respiratory Syndrome (MERS). Suggests that there is. Estimates are the first in a class to quantify asymptomatic infections.

An analysis based on contact tracking data from 349 COVID-19 people and 1,964 close contact persons in Guangzhou, the most populous city in southern China, showed that COVID-19 people were at least as good as they were before the onset of symptoms. Their actual illness, which was found to be infectious, and the elderly (aged 60 and older) were most vulnerable to household infection by SARS-CoV-2.

A survey of people and family living together (not living at the same address) and non-household contacts (eg friends, colleagues, passengers) Infection; infection In addition to case detection and quarantine, timely tracking and close contact quarantine can have a significant impact on reducing the number of COVID-19 cases in households.

The model has been updated to reflect current knowledge about the transmission dynamics of COVID-19, but the authors warn that it is based on a set of assumptions. Symptomatic case It is infectious, has not yet been confirmed, and may affect the accuracy of your estimation.

“Our analysis suggests that pre-symptomatic COVID-19 is highly infectious in individuals and can significantly increase the difficulty of controlling an ongoing pandemic,” he said. Dr. Yang Yang, a collaborator at the University of Florida, says the study. “Active case detection and quarantine combined with comprehensive contact tracking and quarantine is key to keeping infected contacts from getting infected. Incubation periodThis is important when relaxing the lockdown limits for movement and mixing. “

The domestic transmission of COVID-19 is believed to have contributed significantly to the increase in incidents in China after the introduction of blockade measures. However, few studies have evaluated the prevalence of illness at the household level. The previous estimate of household infection is specific to the settings from which the data was obtained and represents the percentage of infections among all contacts tracked. This is due to the fact that the differences in individual exposure histories are not fully taken into account, or that the infection may not always be secondary, and the tertiary, i.e. possible infection between contacts. Sexuality or risk of infection from objects such as clothes, utensils, and furniture.

In this study, the researchers developed an infection model that describes individual-level exposure, tertiary infection, potential exposure to untracked sources, and subclinical infections. 215 primary COVID-19 cases collected by the Guangzhou Centers for Disease Control and Prevention (CDC) (no known sources of exposure or assumed to be infected outside Guangzhou), 134 secondary/tertiary cases, And in the 1,964 survey, in the close contact between January 7 and February 18, 2020, the secondary attack rate of people living together with family members and non-household contacts (infected people The probability of transmitting the disease to susceptible people). Close contact, i.e. tracking and quarantining unprotected individuals who were within 1 meter of a person with COVID-19 within 2 days of onset of symptoms and on days 1 and 14 SARS-CoV-2 Was tested.

This study also modeled the effects of age and gender on the infectivity of COVID-19 cases and their susceptibility to close contact. For the main results, the researchers assumed an average incubation period of 5 days and a maximum infection period of 13 days (including up to 5 days before onset). Of the 349 laboratory-confirmed primary and secondary COVID-19 cases, 19 (5%) reported no symptoms during follow-up.

The analysis estimated that the likelihood of secondary infection-spreading from infected to non-home contacts-was estimated to be 2.4%. The probability of transmission of the virus is higher in those living together and in the family, with an attack rate of 17.1% (or approximately 1 in 6) for those who live at the same address, and 12.4% for the family. (About 1 in 8). member.

“Families, such as parents and older children, may not live at the same address, which may explain why COVID-19 cases are at a lower risk of secondary infections than those living in the same household. No,” says co-author Dr. Natalie Dean of the University of Florida, USA. “It may seem that COVID-19 is less likely to be transmitted at home, but about twice the SARS estimate (4.6-8%) and three times the MERS estimate (4-5%). ), but these data are based on only a few studies.”

This model also has the highest probability of household infection among older people aged 60 and older (28% attack rate or 1 in 4 cohabitants, 18.4% in families or 1 in 5) It is also suggested to be the lowest among those under the age of 20 (attack rate 6.4%, or 1 in 15 cohabitants, 5.2%, or 1 in 20 families).

Estimates also suggest considerable infectivity during the incubation period, comparable and potentially higher during the illness period. One day after exposure (daily probability of infection), family members are 39% less likely to become infected after symptom onset than during the incubation period (OR 0.61), while cohabitants have 41% odds. Although low (OR 0.59), the difference was not statistically significant (Table 3). There was no gender difference in infection risk.

The researchers also calculated the regional reproductive count, the average number of infections a COVID-19 case can develop through both people living together during the entire infection period. Family members, And non-household contacts) was 0.5. However, if case quarantine or contact quarantine were not implemented, the estimated local fertility would increase by 20-50% to 0.6-0.76. If the fertility remains less than 1, infection It cannot spread effectively.

“The relatively low fertility in the absence of case separation reflects a low average number of contacts per person per day, probably due to the stringent control measures in Guangzhou during the study. ”Explains co-author Dr. Qin Long. Guangzhou CDC, Jin of China. “The impact of case separation appears to be moderate, but the high infectivity of the virus during the incubation period suggests that asymptomatic contact isolation may have prevented more infections.” .”

The authors found that only 2 of the 15 asymptomatic cases included in the analysis were considered major cases, and some asymptomatic infections may have been missed, so We are aware of some important limitations, including the inability to reliably quantify the infectivity of the infection. The test was run twice and the test was done at 14 day intervals. In addition, this model might not be accurate because it assumed that asymptomatic infections had the same infectivity as symptomatic cases during the incubation period. It also noted that some imported primary cases may be infected locally and asymptomatic infections or cases may have been missed by contact traces or false negative tests. Therefore, the secondary attack rate may be underestimated. Finally, the rapid isolation of cases and close contact isolation may have limited the number of infections if the cases were ill and affected the accuracy of estimation.

Dr. Virginia Pitzer of Yale School of Public Health (who was not involved in the study) wrote in the linked comment: “The main difference between SARS-CoV-2 and SARS-CoV is the significantly higher probability of infection. Higher during the pre-onset incubation period of SARS-CoV-2, but the onset of SARS-CoV symptoms. There was little or no previous infection, which made SARS-CoV much more controllable due to case separation and contact isolation, in particular the authors COVID-19 in Guangzhou. It is estimated that only 20-50% of the secondary cases could be prevented.”

“This study shows the value of carefully collected contact-tracking data to understand risk factors for transmission and susceptibility. The findings show that the relative importance of pre-onset transmission is: Review the relationship between elderly and susceptibility, a key insight to inform design of intervention strategies.”