key: cord-0789497-hkli7460
authors: Fang, Guanfu; Feng, Jin
title: Is the 2003 SARS epidemic over? Long-term effects of epidemic exposure on mortality among older adults
date: 2021-03-06
journal: China economic review
DOI: 10.1016/j.chieco.2021.101618
sha: ed2f4fb49e86868ae2d54b1cf7507beb299a503f
doc_id: 789497
cord_uid: hkli7460

Infectious diseases put health of millions at risk and induce large socioeconomic costs each year. However, the long-term effects of exposure to infectious diseases on the elderly have received minimal attention. Using data from the Chinese Longitudinal Healthy Longevity Survey, this study adopts a differences-in-differences strategy to evaluate the long-term effects of epidemic exposure on old-age mortality. We find that an intense exposure to the severe acute respiratory syndrome (SARS) epidemic led to an increase in old-age mortality after the SARS outbreak. We provide some suggestive evidence that exposure to SARS increased psychological stress and limitations in physical activities among old people.

Infectious diseases pose a critical and continuing threat to public health. Over the past 20 years, the world has experienced global epidemics of severe acute respiratory syndrome (SARS), avian influenza (H5N1), swine flu (H1N1), meningitis, Ebola, and 2019 novel coronavirus (2019-nCoV). As the worldwide population ages, the adverse effects of infectious diseases may increase due to the vulnerability of old people (R. D. Lee & Mason, 2011) . 1 Although much has been written on the instantaneous physical and socio-economic effects of disease epidemics, limited attention has been given to their long-term effects on old-age health outcomes (Laxminarayan & Malani, 2006; Perrings, et al., 2014) .

This study investigates whether and to what extent the panic caused by SARS, a lethal infectious disease, affected old-age mortality. The disease broke out in November 2002 in the age. The adverse psychological impact of infectious disease epidemics might persist among vulnerable groups.

Second, our study contributes to the literature on the impact of psychological stress on health outcomes (Garfin, Thompson, & Holman, 2018) . There is increasing evidence that traumatic experiences are likely to lead to persistently elevated psychological symptomatology that may linger many years after exposure and increase individual morbidity and mortality (Friedman, Keane, & Resick, 2007) . Marsland, Bachen, Cohen, Rabin, and Manuck (2002) conducted several laboratory experiments and showed that psychological stress affected immune function and predicted infectious disease susceptibility. Nakagawa, et al. (2009) showed that stress arising from the Niigata-Chuetsu earthquake significantly increased long-term mortality from acute myocardial infarction in Japan. Giesinger, et al. (2020) observed that posttraumatic stress disorder associated with the World Trade Center attacks in New York, on September 11, 2001 significantly increased mortality risk. Our study provides new empirical evidence of the long-term effect of stress derived from infectious diseases on the health outcomes of old adults.

The remainder of the article is structured as follows. Section 2 provides background information on the SARS outbreak. Section 3 introduces the data sets and empirical strategies.

Section 4 presents the empirical results. The last section offers the conclusions.

SARS was the first major new infectious disease of the 21st century and was unusual in its high morbidity and mortality rates. It started in the Chinese province of Guangdong in November 2002 and was recognized as a public health emergency of international concern in mid-March SARS is caused by a new coronavirus. It is transmitted primarily through droplets and its incubation period can range from 7 to 10 days. Infected patients may develop symptoms such as high fever, cough, dyspnea, lung disease, headache, muscle stiffness, loss of appetite, diarrhea, and disturbance of consciousness. No licensed pharmaceutical treatment specific for SARS was available during the epidemic. Thus, health authorities had resorted to traditional control tools of microbiology, including case isolation, infection control, and contact tracing.

Several aspects of the disease led to widespread anxiety during the outbreak (Smith, 2006) .

First, the cause of the outbreak remained unclear during the epidemic. Scientists had limited knowledge about the identity and nature of the pathogen. Thus, the probability and means of infection were difficult to be determined. People only knew that SARS was an airborne infectious disease with a high case-fatality ratio of approximately 10 percent. Second, uncertainties remained about the degree of effectiveness of specific interventions or measures to reduce the probability or consequences of infection (Lau, et al., 2005) .

The SARS risk was widely perceived as fatal (J. Liu, Hammitt, Wang, & Tsou, 2005) .

Surveys conducted in Taiwan showed that the perceived fatality of SARS was 4.1 on a scale from 0 (no fatal) to 5 (extremely fatal) (J. Liu, et al., 2005) . People hoarded all possible protective equipment during the outbreak. Cold medicine was sold quickly. The price of Isatidis Radix, a traditional Chinese antiviral remedy, increased by 800 percent in several areas (Bennett, Chiang, & Malani, 2015) . People rushed to purchase white vinegar and garlic, which were thought to prevent viruses. Thus, the prices of these commodities also soared. Public places were disinfected several times a day. People strongly eschewed restaurants, trains, and shopping centers (Bennett, et al., 2015) . Classes were suspended for millions of primary and secondary school students. In addition, quarantine and inspection stations were established on major commuting roads. Travelers were required to complete health declaration forms and temperature checks. All suspected SARS patients were required to remain isolated for monitoring and treatment, up to a maximum of 21 days (Siu & Wong, 2004) .

The social cost incurred due to the epidemic was large. The estimated global macroeconomic impact of SARS was approximately US $3-10 million per case (Chou, Kuo, & did not. Lam, et al. (2009) revealed that the adverse psychological impact of SARS persisted and remained clinically significant among the survivors during the 4-year follow-up. Hawryluck, et al. (2004) found that about one-third of survey respondents who were subject to quarantine for SARS exhibited symptoms of posttraumatic stress disorder and depression. They also showed that the longer duration of quarantine was correlated with an increased incidence of psychological disorders.

Our data come from the CLHLS, which is collected by Peking University. The first round of the CLHLS was conducted in 1998. Six additional panels were collected in 2000, 2002, 2005, 2008, 2011, and 2014 . The survey covered 23 of 31 Chinese provinces, representing 95 percent of the total population of mainland China. Half of the counties and cities were randomly chosen in each of the provinces. Detailed sampling procedures can be found elsewhere (Gu, 2008; Zeng, 2008) .

The survey team aimed to interview all centenarians in selected counties or cities, along with one nearby octogenarian and one nonagenarian matched in accordance with geographical unit and sex. Deceased and lost respondents were replaced with new interviewees. The age and sex of each new interviewee at a subsequent wave were based on the person who was lost to follow-up or deceased within survey intervals. Since 2002, cohorts aged 65 to 79 had also been followed and randomly chosen from the neighborhood of centenarians on the basis of pre-designated agesex ratios. The attrition rate of the CLHLS was moderate. Approximately 12-20 percent of respondents were lost to follow up depending on the survey year.

The data contain a wealth of information that covers topics such as economic activities, education outcomes, family dynamics and relationships, mental status, and cognitive ability. The CLHLS gathered mortality information for the respondents who died between waves in interviews from various sources. Death certificates were used when available, otherwise the reported date of death from a close relative was used and validated using neighborhood registries (Gu & Dupre, whether the respondent surveyed in the last wave was deceased within certain time periods (survey interval; one, two and three years). We also calculate survival time of respondents who died between 1998 and 2014. For the survivors, survival time was the days from the last interview date to the current interview date. We then recode all survival time longer than 850 days as 850.

The survey contains five questions about psychological status: (1) Do you feel fearful or anxious? (2) Do you feel lonely and isolated? (3) Can you make your own decisions concerning personal affairs? (4) Do you feel useless? (5) Do you feel as happy as when you were young?

Each question has five response options, namely, always, often, sometimes, seldom, and never. We create five dummy variables to indicate whether the respondent had these feelings (always/often/sometimes/seldom vs. never).

The survey also collects information on limitations in physical activities. We set six binary variables indicating whether the respondent needed help in performing any of six basic daily activities (bathing, dressing, toileting, indoor movement, continence, and eating). In addition, the survey gathers information on health behaviors including diet, smoking, substance use, and physical activity. We define five binary variables indicating whether the respondent reported certain health behavior (eating fruit every day, eating vegetables every day, smoking, drinking, and exercising regularly). 2

The information on the morbidity, mortality, and duration of SARS is collected from one of the major search engines in China (www.sohu.com) and the WHO website. We quantify SARS exposure of respondents as the duration of SARS in their province. We choose this measure because the long duration of exposure to SARS could be one of the causes of psychological problems. For example, prolonged exposure to the SARS epidemic might be associated with a long period of psychological distress. The duration of SARS in each province is calculated from the report of the first SARS case in this province until June 25, 2003, when WHO removed mainland China from the list of SARS epidemic areas. We also use the morbidity and mortality rates of SARS epidemic in each province as alternative measures of SARS exposure. In addition, we collect SARS confirmed cases for each prefecture from various sources, including news media, research papers, and government websites. We conducted additional analysis using the prefecture level SARS information in the robustness part.

In our analysis of mortality outcomes, we use the full sample (survivors and the deceased) in waves 1998-2011. We exclude 11,102 respondents who are lost to a subsequent follow-up survey because their mortality information is unavailable. We then exclude 135 respondents aged less than 65 because the eligible respondents in the CLHLS are those old people aged 65 and over.

We further exclude 264 respondents whose education information is missing. Our final sample includes 59,481 observations from 31,707 respondents. Owing to the data availability, the sample sizes for the regression analysis vary with outcomes. Table 1 provides descriptive statistics of the main variables. The average age was 87.8. Women accounted for over half of the group, 56.5 percent lived in rural areas, and 63.2 percent had no schooling. Among the old adults surveyed in the current wave, 38.8 percent died within the adjacent survey interval, 12.7 percent within one year, 28.9 percent within two years, and 45.7 percent within three years. In the part of mechanism analysis, we further restrict the sample to all survivors in waves 1998-2005. Table A2 provides descriptive statistics of other health outcome variables. The sample sizes for the regression analysis also vary with outcomes due to data availability. Figure A1 presents a heated map of SARS duration in China. We examine the correlation between SARS duration and socioeconomic factors at the provincial level in Table 2 . In general, we find small and statistically insignificant correlations between SARS duration and socioeconomic factors, including the mortality and morbidity of infectious diseases, health care resources, and local economic condition. The mortality rate is defined as the proportion of old adults deceased during the last survey interval. The mortality rate of provinces with high SARS exposure was lower than that of provinces with low SARS exposure before 2003. However, the mortality rate of provinces with different SARS exposure converged after the 2003 SARS epidemic. The mortality rate of provinces with high SARS exposure was sometimes even higher than that of provinces with low SARS exposure. This finding suggests that the health outcomes of old adults might worsen due to exposure to the lethal infectious disease. 3 Figure 2 shows the adjusted patterns of survival curves stratified according to exposure subtypes. Old people who were surveyed after 2003 had a lower mortality risk than those surveyed before 2003. However, the decrease in the mortality risk of old people in provinces with low SARS exposure was much larger than that in provinces with high SARS exposure. The difference in the decrease in mortality risk increased as time went by and became stable after two years. long-dash line stands for provinces with less than 70 days of SARS exposure before the SARS outbreak, the dash-dot line for more than or equal to 70 days of SARS exposure before the outbreak, the solid line for less than 70 days of SARS exposure after the outbreak, the short-dash line for more than or equal to 70 days of SARS exposure after the outbreak. The shadows around the survival curves indicate a 95% confidence interval.

We use a DID strategy to identify the effects of SARS exposure on the health outcomes of old people. We compare the relative change in the health outcomes of old people in the post-epidemic period relative to the pre-epidemic period among provinces that had various levels of SARS epidemic intensity. The difference between our estimates and a standard DID strategy is that we employ a continuous measure of the intensity of treatment (i.e., SARS epidemic intensity) and thereby capture more variation in the data (Nunn & Qian, 2011) .

We estimate the following equations:

where Y i t represents a given health outcome (e.g. mortality, physical health, and mental health In our analysis of old-age mortality, the variable Post takes a value of 1 for respondents surveyed in 2002 because their mortality information during the later survey interval, one/two/three years was observed after 2003. is a vector of observed individual variables that may vary over time (e.g., five-year age groups, gender, hukou status, and years of education). 4 α p stands for province fixed effects, and λ t represents survey year fixed effects. ε ipt is an error term. To allow for arbitrary correlations in the outcomes of residents in the same province, we cluster the standard errors at the province level (Bertrand, Duflo, & Mullainathan, 2004) .

The DID coefficient is β 1 , which measures the change in the effect of SARS exposure preand post-epidemics. Any common macro changes are picked up by the time dummy. This approach identifies a change in the health outcome of old people due to the disease shock. The key

identifying assumption is that without the SARS epidemic the trend in the outcome would have been the same across provinces whatever the epidemic intensity is. Treatment induces a deviation from this parallel trend.

We adopt the accelerated failure time (AFT) model to examine the effect of SARS exposure on survival time of old people. The AFT model is a parametric linear model for survival analysis.

The dependent variable is the log of survival time and independent variables are the same as those in the OLS specification. A key assumption of the AFT model is the distribution of survival times.

We use different distribution assumptions (log-normal, log-logistic, and gamma distribution) to examine the sensitivity of model specifications. 5 4 Empirical results p-values are reported in brackets. ***significant at 1% level, **at 5%, *at 10%. (1) to (3) present estimates from accelerated failure time models using the log-normal, log-logistic, and gamma distributions, respectively. Other control variables include a dummy for females, a dummy for rural areas, and years of education. Standard errors are clustered by province. ***significant at 1% level, **at 5%, *at 10%.

We conduct a range of robustness tests, namely, checking for parallel trends prior to treatment assignment, a permutation test using randomly assigned treatment intensity, excluding the influence of other policies that might have been running at the same time, and testing for sample selection and functional form. In general, our findings remain robust. We report the results in the appendix.

Parallel trends. -A potential problem with DID is that it may confound the dynamic effects of SARS exposure with pre-existing differences in time trends across treated and untreated groups.

In other words, provinces considerably exposed to SARS might have experienced deterioration in health outcomes after the epidemic due to differences in time trends that preceded SARS. Three approaches are considered to address this issue. SARS exposure are small and statistically insignificant, suggesting that the treatment and control provinces followed similar time trends before the SARS outbreak. However, the mortality risk in provinces with high SARS exposure increased much faster than that in provinces with low SARS exposure after the SARS epidemic.

Third, we conduct a permutation test (Rosenbaum, 2007) . Specifically, we draw 1,000 placebo treatment days for each province from the full support of the potential days of the estimation sample, 0-222, and then re-estimate the baseline models. Figure A2 presents the histogram graphs for these estimates. The thick line indicates the estimation results using the true treatment level. The placebo treatment effect estimates are significantly different from the true estimate. The permutation test indicates that the sign and significance of our estimates are not merely driven by provincial differences unrelated to the effect of SARS exposure. Overall, the three approaches lend further credence to our identification strategy.

Possible contamination from other policy changes. -Our estimation strategy exploits an exogenous disease shock. We therefore need to check that the change we observe was due to SARS exposure rather than other shocks or policies that might have been running at the same time.

One potential policy candidate was the New Rural Cooperative Medical Scheme (NRCMS), which was introduced around 2003 to expand the coverage of insurance in rural households. It provided allowances to rural residents, and its enforcement varied across provinces. It was possible that the enforcement of the NCRMS was correlated with both SARS exposure and health outcomes, therefore biasing our baseline estimates.

To test this possibility, we include in the regression a dummy variable indicating whether the respondent was enrolled in the NRCMS. The CLHLS did not collect information on social insurance until 2005. We assume that the respondent was not enrolled in the NRCMS before J o u r n a l P r e -p r o o f Table 3 .

In our main analysis, we use robust standard errors clustered at the provincial level. We have 23 clusters. The small number of clusters might bias our standard errors downwards. 9 To address concerns about the small number of clusters, we perform 1,000 draws of a wild-cluster bootstrap percentile t-procedure suggested by Cameron, Gelbach, and Miller (2008) . In Panel H of Table A3 , we present robust standard errors in parentheses as well as two-tailed wild cluster bootstrap p-values in square brackets. Our results remain robust.

Sample selection. -One might wonder whether the sample selection process biases our estimates. We drop those old adults who were lost to follow-up in our main analysis. If the sample attrition was not orthogonal to SARS exposure, our results might be biased. We include the lost-to-follow-up sample and formally test whether SARS exposure affected the sample attrition rate. We set a dummy variable indicating whether an old man dropped out from the study. The estimates presented in Column (1) of 1998, 2000, and 2002 . Therefore, our assumption is reasonable. 8 Possible sources of financial support include retirement wages, spouses, children, grandchildren, relatives, local government or community, work, and others. The types of marital status include single, married, widowed, divorced, separated, and cohabiting with a partner. 9 There is no clear threshold for too few clusters. The number could vary between 20 and 50 (Cameron & Miller, 2015) .

Journal Pre-proof (5) of Table A4 are similar to our baseline results.

Measures for SARS exposure -In our previous analysis, we have assumed that exposure to SARS follows a simple linear function of SARS duration. As a robustness check, we quantify exposure to SARS as infection rate ranking, death rate ranking, infection rate, and death rate. 10 We report estimates using alternative measures of SARS exposure in Panels A to D of Table A5 and find qualitatively identical results. The estimates using death-related measures are less significant than others. A potential explanation is that the number of deaths may not be a good measure for SARS exposure. During the epidemic, people in provinces with SARS cases but no SARS deaths might have experienced more severe psychological distress than those in provinces with no SARS cases did.

One might doubt that SARS exposure at the provincial level contains large measure errors.

There could be substantial heterogeneity of actual SARS exposure within the same provinces.

Cities within the same province may have differential capacity to avoid harm from the epidemic at the local level. In addition, variations in SARS exposure at the provincial level may confound with some unobserved factors at the provincial level, such as clan culture, social normal, property institutions, and experiences of government officials. We formally test whether the level of SARS exposure measurement biases our estimates.

We rerun our baseline regressions using the prefectural level SARS infection rate as an alternative measure of SARS exposure and control for city, birth year, female, urban area, and survey wave dummies. The standard errors are clustered at the city level. We report the estimates in Panel E of Table A5 . Reassuringly, the estimated effect of SARS exposure changes little. Overall, these estimates suggest minimal bias induced by the measurement of SARS exposure.

We have established that old-age mortality risk increased faster in provinces with high SARS exposure than in provinces with low SARS exposure. Due to data limitation, we cannot be conclusive about the underlying mechanism. In this subsection, we provide some suggestive evidence that old people experienced a decline in their psychological and physical well-being due 10 The infection rate in Beijing is much larger than those in other provinces. We exclude Beijing when using the linear function of infection rate and death rate to measure SARS exposure. The estimates are similar if we include the Beijing sample and assume that the Beijing province has the same infection rate as Guangdong Province, which has the second largest infection rate.

to SARS exposure. We focus on surviving respondents surveyed before or during 2005. The corresponding estimates should be interpreted with caution because the health outcomes of old adults who died within the survey interval were not observed. If old adults died within the survey interval due to sharp declines in psychological and physical health status, our estimates should be downward biased. We do not include respondents surveyed after 2005 for the same reason. (2) Do you feel lonely and isolated? (3) Can you make your own decisions concerning personal affairs? (4) Do you feel useless? (5) Do you feel as happy as when you were young? The dependent variables are dummy variables indicating whether the respondent had these feelings (always/often/sometimes/seldom vs. never). Other control variables include a dummy for females, a dummy for rural areas, and years of education. The sample is restricted to old adults surveyed before and in 2005. Standard errors are clustered by province. ***significant at 1% level, **at 5%, *at 10%. (1) to (5) are dummy variables for anxiety and fear, isolation and loneliness, making own decisions, uselessness, and happiness, respectively. The results indicate that old people surveyed in provinces with high SARS exposure were more likely to feel anxious and fearful, lonely and isolated, and useless than those in provinces with low SARS exposure. SARS exposure had no obvious effect on the likelihoods of making their own decisions and being as happy as when the respondents were young. (1) to (6) are dummy variables indicating whether the respondent needed assistance in bathing, dressing, toileting, indoor movement, continence, and eating. Other control variables include a dummy for females, a dummy for rural areas, and years of education. The sample is restricted to old adults surveyed before and in 2005. Standard errors are clustered by province. ***significant at 1% level, **at 5%, *at 10%.

We have found that SARS exposure was associated with psychological distress, physical disability, and mortality risk. A potential explanation is that the exposure to traumatic stressors created long-lasting psychological distress among the elderly and then increased their risk of disability and mortality. This explanation is consistent with previous studies on the associations among psychological distress, physical activity, and mortality (Cuijpers & Schoevers, 2004) . For example, Schoevers et al. (2000) found that psychological distress deterred participation in physical activity among older adults in Australia. Gu and Feng (2018) found that a high level of psychological distress was associated with a high mortality rate and poor health among the oldest old in China. Feng, Hoenig, Gu, Yi, and Purser (2010) showed that old adults with limitations in activities of daily living had significantly higher mortality risk than those without any limitations in China. (1) is the amount of daily staple food consumption. The dependent variables in Columns (2) to (6) are five binary variables indicating whether the respondent reported certain health behavior (eating fruit every day, eating fruit every day, smoking, drinking, and exercising regularly). Other control variables include a dummy for females, a dummy for rural areas, and years of education. The sample is restricted to old adults surveyed before and in 2005. Standard errors are clustered by province. ***significant at 1% level, **at 5%, *at 10%.

An alternative explanation is the change in health behaviors. SARS exposure might change old adults' attitudes toward health, thus altering their actions to maintain, attain, or regain good health and to prevent illness. Table 7 presents estimates for health behaviors including diet, smoking, substance use, and physical activity. The coefficients are small and statistically insignificant, which suggests minimal impact of SARS exposure on these health behaviors.

Overall, there is little evidence that the associations between SARS exposure and old-age mortality could be explained by changes in health behaviors.

We have established that the mortality rate of old people increased faster in areas with high SARS exposure than those in areas with low exposure. An important question is whether the long-term impact of SARS exposure or whether susceptibility and response varied. We study this issue in this subsection. Table 8 presents heterogeneous effects of SARS exposure on old-age mortality across individuals. Men and women may differ in their reaction to psychological distress. Women may be more vulnerable than men to the effect of external stress, such as wartime events, loss of friends and relatives (Sibai et al., 2001) . Columns (1) and (2) results suggest that although SARS exposure had significantly negative effects on both men and women, the effect was more pronounced for women. We use the method of seemingly unrelated estimation to test the equality of coefficients across gender and find that the difference in the estimated effects is statistically significant.

Education is an important determinant of the health outcomes of old adults (Luo, Zhang, & Gu, 2015) . The response of people to traumatic events may vary by their educational level. Those with low levels of education might lack protective knowledge or resources to cope with stress arising from the SARS epidemic. Columns (3) and (4) show estimates for literate and illiterate people, respectively. The estimates indicate that the illiterate old adults, but not the literate, had a significantly elevated mortality risk because of exposure to the SARS epidemic. The difference in the estimated effects for the illiterate and the literate is statistically significant. (1) and (2) control for years of education and a dummy for rural areas. The regressions in Columns (3) and (4) control for a dummy for females, a dummy for rural areas, and years of education. Standard errors are clustered by province. ***significant at 1% level, **at 5%, *at 10%. Table 9 presents heterogeneous effects of SARS exposure across regions. The impact of SARS exposure may differ in urban and rural areas. On the one hand, urban residents were exposed to SARS more than rural residents were because most of the infected patients lived in urban areas. On the other hand, urban residents had more advantages than rural residents when coping with the SARS epidemic. Urban residents had higher income and could afford better social J o u r n a l P r e -p r o o f services. By contrast, timely care was less available to rural residents due to lack of health insurance or regular health care facilities. We estimate separate models for rural and urban residents. The results presented in Columns (1) and (2) J o u r n a l P r e -p r o o f Journal Pre-proof J o u r n a l P r e -p r o o f Journal Pre-proof Note: All cells report the DID estimates from separate regressions. We interact the survey wave dummies with socioeconomic factors in 2003 to control differential trends in Panels A to C. Panel A controls for differential trends associated with morbidity and mortality rates of notifiable infectious diseases. Panel B controls for differential trends associated with numbers of physicians, nurses, beds, and general hospitals. Panel C controls for differential trends associated with GDP, government expenditure, population, and proportion of old adults aged (1), we additionally include the sample who was lost to follow-up during the survey, and the dependent variable is a dummy variable indicating the respondent dropped out from the study. In Columns (2) to

(3), we exclude those aged less than 78 in our baseline sample. Other control variables include a dummy for Note: All cells report the DID estimates from separate regressions. Panels A and B use the whole sample. Panels C to E exclude the Beijing sample. In Panels A to D, other control variables include dummies for province, a dummy for females, a dummy for rural areas, and years of education, and standard errors are clustered by province. In Panel E, other control variables include dummies for cities, a dummy for females, a dummy for rural areas, and years of education, and standard errors are clustered by city. ***significant at 1% level, **at 5%, *at 10%.

Journal Pre-proof

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Preparing for the next pandemic

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