A new study published on the preprint server medRxiv* in June 2020 explains the significant risk posed by poverty and the success of measures to counter COVID-19. The research sets out a model for more focused but productive interventions in high-risk groups that can reduce mortality rates significantly.
The Pandemic and Its Fallout
The COVID-19 pandemic began in Wuhan, China, in December 2019, and rapidly spread across provincial and national boundaries to become a major cause of sickness and death in over 188 countries and territories. As of June 8, 2020, it has caused over 7 million cases and 400,000 deaths.
The speed and scale of the outbreak led to the imposition of unprecedented lockdown measures, including local, interregional, national, and international travel. The current study stems from Israel, where, from March 9, 2020, international travelers cannot enter unless they can prove they can stay isolated at home for two weeks. Following this, all schools and daycare centers were closed down, and workplaces operated at below one-third of their capacity. March 26 saw the restriction on travel further than 100m from home for any but essential reasons. Holiday celebrations were also banned following the announcement of three lockdowns to prevent crowds from gathering.
This news article was a review of a preliminary scientific report that had not undergone peer-review at the time of publication. Since its initial publication, the scientific report has now been peer reviewed and accepted for publication in a Scientific Journal. Links to the preliminary and peer-reviewed reports are available in the Sources section at the bottom of this article. View Sources
The measures proved successful in limiting viral spread, with a drastic decline in the number of cases reported. However, economic losses were both widespread and acute, with some estimates reporting that the gross domestic product could fall by 1.7-13.1% in different countries, with a lockdown of 1.5 to 3 months in duration. This has led to more intensive research into how COVID-19 transmission works, to optimize control where it is needed and allow relaxations where lockdowns are unnecessary.
Disease Transmission Varies with Human Factors
Factors that affect the risk of infection and symptomatic disease include demographics, education, host, and agent factors. The enormous disparity in the severity of the disease between different groups has brought the effect of age to the fore in current research. It has also made it necessary to identify the truly high-risk groups, where interventions can lessen not just the infection rate but mortality.
Identified groups include:
- Older people who have many times the risk of younger people for infection, severe disease, and death.
- Sicker people such as hypertensives and diabetics may be at 3-21 times the risk of healthy people.
- Poorer populations who often live in dense clusters, and have significantly less access to healthcare services. This puts them at greatest risk during any health-related crisis.
- Larger families are also at risk because of the high rate of household viral spread for any respiratory infection.
Another key component affecting the extent of viral spread is human mobility. The researchers comment, “As many as 95% of cases are unreported.” They used large-scale location data from mobile phone records to track the locations of more than 3 million users, and therefore their mobility patterns, how these complied with current restrictions, improve the data available for epidemiologists to form their theories, and to pick up hotspots.
Effect of Mobility and Poverty on Viral Spread
The current study looks into how these factors affect the viral spread. After analyzing how mobility and poverty affect viral spread, as judged by the mobile phone location data, they incorporated the mobility data into transmission models built up by age group, to identify the optimal plan to bring down mortality due to COVID-19.
The movements of over 3 million citizens in 2,600-plus zones were described hour by hour over the period February 1, 2020, to May 16, 2020. This spanned the period from one month before the outbreak in Israel to the point when there were 16,600 reported cases. This period included several restrictions on movement, which were applied and relaxed in turn.
The researchers described the mobility index (MI) as the daily proportion of individuals who traveled more than 1.5 km away from home. While the MI dropped steeply with restrictions in place, the decline was non-uniform. While people with low socioeconomic status (SES) had the lowest MI ordinarily, this changed, so that all groups had similar MIs once lockdowns began. During the restriction period, those with the highest SES had the lowest MI.
The clustering of travel to zones where the SES of the inhabitants matched those of the travelers was a noticeable feature of the population both before and during the lockdown, but became accentuated in the latter period.
The researchers next narrowed down the period of observation to 3 periods. Namely, February 13-March 26, March 27-April 20, and April 20-May 20 corresponding to the early pre-lockdown phase, the lockdown phase, and post-lockdown. While the infection rate was fairly uniform over the first of these periods, the second period saw about 70% of cases coming from poorer communities, particularly orthodox Jews. In the third period, again, about 80% came from low SES zones, mainly orthodox Jews and Arab residents.
The MI was correlated with the growth of the disease by about 80% with a lag of 12-14 days between the two. This lag includes the time from the infection, through the time of testing to the arrival of test results.
The daily mobility data added into a model for viral transmission that included age, region, and risk factors as stratifying factors. The model was found to accurately reflect actual trends in Israel’s epidemic, such as peaking during March 17-25. Secondly, the model showed that mobility had to be considered to capture the actual growth of the outbreak in spatial and time-related dimensions while inflating viral spread estimates.
Focused vs. General Lockdown
The model projected the total deaths for 1-3 years in lockdown conditions. They simulated three plans, each triggered by a daily threshold of COVID-19 incidence in each of 250 regions. These envisaged a complete lockdown, daycare and school lockdown, and high-risk group lockdown. Finally, they compared these projections with that following a nationwide lockdown in response to a similar national incidence. The compliance rates in each case were arrived at by that seen with earlier lockdowns.
The results showed that focused strategies were much more effective in reducing daily deaths per day of lockdown, and particularly the high-risk group lockdown. With about 85% of cases going unrecorded, and if about 5 cases are reported per 10,000 during the lockdown, the efficiency of the high-risk quarantine strategy works about 5 times better than a universal lockdown.
In terms of mortality reduction, a high-risk quarantine strategy would result in about 4,500-4,900 deaths in the above conditions, but closing down the movement of children would still leave about 7,900-10,500 deaths in one year. If the lockdown threshold is over 5/10,000, the high-risk focused strategy is 2.2-5.5 times less restrictive but just as effective as a global lockdown.
The study also suggests a higher spread among people of lower SES, with increased mobility and with increased frequency of interactions. The last is more common among people of high SES and may explain the higher spread in developed countries so far.
Implications for Future Policies
The non-uniform dissemination of COVID-19 in the population calls for localized, dynamic strategies that change over time and focus on high-risk groups. This is effective in reducing the number of deaths but helping most people go about their daily routine as normal. A temporary lockdown of high-risk groups is found to be the best way to reduce mortality while maintaining normality.
The investigators say, “Our transmission model predicted that rather than nationwide lockdowns, applying temporal and localized lockdowns that focus on groups at high risk can substantially reduce mortality. Our findings can help policymakers worldwide identify hotspots and apply designated strategies against future outbreaks.”
This news article was a review of a preliminary scientific report that had not undergone peer-review at the time of publication. Since its initial publication, the scientific report has now been peer reviewed and accepted for publication in a Scientific Journal. Links to the preliminary and peer-reviewed reports are available in the Sources section at the bottom of this article. View Sources
Article Revisions
- Mar 21 2023 - The preprint preliminary research paper that this article was based upon was accepted for publication in a peer-reviewed Scientific Journal. This article was edited accordingly to include a link to the final peer-reviewed paper, now shown in the sources section.
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