Early Epidemiological Features and Trends of the COVID-19 Outbreak in Southeast Asia: a Population-Level Observational Study

doi:10.21203/rs.3.rs-34707/v1

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Early Epidemiological Features and Trends of the COVID-19 Outbreak in Southeast Asia: a Population-Level Observational Study

https://doi.org/10.21203/rs.3.rs-34707/v1

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Background: The global outbreak of coronavirus disease 2019 (COVID-19) has been ongoing in Southeast Asia since 13 January 2020. To guide intervention strategies and summarize beneficial experience, we describe the early epidemiological features and evaluate the trends of the COVID-19 outbreak in Southeast Asia.

Methods: In this population-level observational study, we compiled a list of individual patients with COVID-19 and daily country-level case counts between 13 January 2020 and 16 March 2020 in Southeast Asia. Relevant spatiotemporal distributions, demographic characteristics and short-term trends were assessed.

Results: A total of 1,346 confirmed cases of COVID-19 with 217 (16.1%) recoveries and 18 (1.3%) deaths were reported in Southeast Asia as of 16 March 2020. The early transmission dynamics was fit with an exponential regression model: y=0.30e0.13x (p<0·01, adjusted R2 = 0.96). Using this model, we predicted that the cumulative number of reported COVID-19 cases in Southeast Asia would exceed 10,000 by early April 2020. A total of 74 cities across 8 countries in Southeast Asia were affected by COVID-19 and most of the confirmed cases were located in 5 international metropolitan areas. Demographic analysis conducted on 925 confirmed cases indicated a median age of 44 years and a sex ratio of 1.25. Median age of local patient population was significantly higher than that of general population in corresponding country (p<0·01), whereas sex ratio did not significantly differ.

Conclusions: The situation of COVID-19 endemic in Southeast Asia is intricate and not optimistic in the short-term. Dynamic disease surveillance and targeted intervention strategies are urgent.

Infectious Diseases

COVID-19

epidemiological characteristics

trend

Southeast Asia

An unknown infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, China, in December 2019 [1]. The disease was later officially named coronavirus disease 2019 (COVID-19). The World Health Organization (WHO) declared COVID-19 a pandemic on 11 March 2020 due to its rapid global spread [2].

The global outbreak of COVID-19 has been ongoing in Southeast Asia since 13 January 2020, making Southeast Asia to be the first affected region outside China. In the context of globalization, regional disease surveillance is essential because it contributes to the formulation of responses to such emerging infectious diseases [3]. Consequently, it is imperative and necessary to conduct epidemiological analysis using early surveillance data of COVID-19 outbreak in Southeast Asia to guide formulation of intervention strategies.

Data Compilation

We closely monitored updates from press releases and situation reports on COVID-19 issued by health authorities from each Southeast Asian country and the WHO between 13 January and 16 March 2020. Multilingual ability of our research team enabled us to collect all types of data including daily case counts, outbreak maps and basic demographic characteristics such as age, gender and nationality in real time and effectively. A list of individuals who tested positive for COVID-19 which included 925 confirmed cases and 18 deceased cases was compiled by our team because health authorities in Malaysia and Indonesia did not disclose relevant details from 13 March 2020. The median ages and sex ratios of general populations were extracted from World Population Prospects 2019 of the United Nations and World Bank Open Data.

Statistical Analysis

Research paradigm of descriptive epidemiology was applied to reconstruct the actual spatiotemporal and population distributions of COVID-19 outbreak in Southeast Asia. An epidemic curve and semi-logarithmic line graph were constructed by date of report. A spatial distribution of confirmed cases was illustrated with marked maps. We also assessed the age, sex and nationality of individuals with COVID-19 and those who died of it. Crude recovery or fatality rates were calculated based on cumulative counts reported.

Moreover, a paired t-test and independent-samples Mann-Whitney U test were used to compare median age and sex ratio between patients and the general population. The tests were also used to make comparisons between deceased and surviving cases. The exponential regression model was constructed to estimate short-term trends of incidence, and subsequently tested its statistical significance and goodness-of-fit.

Hypothesis testing, analysis and model building were performed using SPSS statistical software version 26.0 (IBM Corp., Armonk, NY, USA). P value of less than 0·05 was considered to be statistically significant.

Ethical Approval

The ethical approval or individual consent was not applicable.

A total of 1,346 confirmed cases of COVID-19 were reported in Southeast Asia as of 16 March 2020, out of which 217 patients have recovered and 18 patients have died. The crude recovery rate and fatality rate were 16.1% and 1.3%, respectively.

Temporal Distribution

An epidemic curve of confirmed cases (by date of report) indicated that there were two distinct phases: (1) 13-29 February 2020 (before March) and (2) 1-16 March 2020 (in March). Cases of COVID-19 during the first phase of infections were relatively mild with only a few confirmed cases reported daily, and most of the cases were from Singapore and Thailand. However, in the second phase, the daily reported numbers of confirmed cases increased rapidly especially in Malaysia. The highest jump in new COVID-19 infections with a single day increase of 190 new cases was recorded in Malaysia on 15 March 2020 (Figure 1).

A semi-logarithmic line graph of distribution of cumulative cases over time revealed that transmission rate of COVID-19 in Southeast Asia significantly increased at the population level in the beginning of March 2020, whereas transmission rate in China remained stable (Figure 2).

An exponential curve was used to fit temporal distribution of cumulatively reported cases in the second phase. We obtained the following regression model: y=0.30e^0.13x(y is the cumulative number of confirmed cases in the second phase and x is the number of days from the time when the first case was reported in Southeast Asia). Results of analysis of variance (ANOVA) indicated that this model was statistically significant (F=355.48, p<0·01) and the adjusted R²was equal to 0.96. The cumulative number of confirmed cases of COVID-19 in Southeast Asia was predicted to exceed 10,000 by early April 2020 using the model (Figure 3).

Spatial Distribution

Eight countries in Southeast Asia (except Laos, Myanmar and Timor-Leste) had reported confirmed cases of COVID-19 by 16 March 2020. Malaysia (553), Singapore (243) and Thailand (147) were leading with COVID-19 infections, accounting for 70.1% of the total cases reported in Southeast Asia. Notably, Singapore had the highest number of recovered cases (109) with a crude recovery rate of 44.9%, whereas most deaths occurred in the Philippines (12) and Indonesia (5) with crude fatality rates of 8.5% and 3.7% respectively.

In the first phase previously mentioned, 69.3% of the confirmed COVID-19 cases were largely concentrated in two major international metropolises, Singapore and Bangkok (Figure 4A). Onset focus areas of COVID-19 infections expanded to other international metropolises in this region including Manila, Kuala Lumpur and Jakarta. Number of affected cities rose to 74, giving the pandemic a "cancer metastasis-like" spatial distribution especially in the Malay Peninsula (Figure 4B).

Demographic Characteristics

Our sample size for demographic analysis was 925 patients with COVID-19. Participants included two patients, 1 Cambodian with unknown age and 1 Indonesian with unknown sex because this information was not published by health authorities in Cambodia and Indonesia. Meanwhile, the missing values of 104 cases’ age in Malaysia were imputed with a stochastic simulation method based on the age distribution of confirmed cases as of 13 March 2020 issued by the Ministry of Health, Malaysia [4]. Basic demographic characteristics analyzed are summarized in Table 1.

Table 1. Basic demographic characteristics of the study population.^a

	Confirmed cases (n=925)	Deceased cases (n=18)
Age, years	44 (32-58)	58 (51-67)
Sex
Male	514 (56%)	11 (61%)
Female	410 (44%)	7 (39%)
Nationality
Local	750 (81%)	15 (83%)
Foreign	175 (19%)	3 (17%)
Underlying conditions	N/A	13 (72%)

a. Data are median (IQR) or n (%).

Demographic analysis of confirmed cases (n=925) revealed that COVID-19 patients were largely aged between 20 and 69 years, and this age group constituted 88.9% of the total confirmed cases in Southeast Asia. In addition, the proportion of COVID-19 cases among elderly individuals aged 60 years and over was 21.9% (Figure 5).

Ages of individuals with COVID-19 in Southeast Asia ranged from 0.25 to 96 years, with a median age of 44 years. Our analysis recorded a total of 514 males and 410 females, with a sex ratio of 1.25. The median age and sex ratio for populations with confirmed COVID-19 cases (PWCC, both whole and local), and the general population (GP) in each country are represented in Figure 6A and 6B respectively. Moreover, median age of PWCC (local nationals) was significantly higher than that of the corresponding GP (Paired t-test; p<0·01), whereas sex ratio did not significantly differ between the two population groups (Paired t-test; p>0·05).

Confirmed COVID-19 cases in Southeast Asia were predominantly local cases (81.1%). Cases among foreign nationals were chiefly from China (7.2%), Europe (5.0%) and other Asian countries (4.4%). Vietnam was one of the countries with the highest proportion of COVID-19 cases among foreign nationals (34.4%) whereas Brunei had the least (1.9%).

Median age of 18 COVID-19 deaths recorded during the study period was 58 years, which was significantly higher than that of surviving cases (43 years) (independent-samples Mann-Whitney U Test; p<0·01). Thirteen (72.2%) patients who died from COVID-19 infection had underlying conditions prior to diagnosis of COVID-19, mostly diabetes and/or chronic cardiovascular diseases (10 cases). The remaining 3 deaths respectively combined with dengue fever, asthma and kidney transplantation.

This study retrospectively analyzed early data for COVID-19 outbreak in Southeast Asia at the population level. Relevant spatiotemporal distributions and demographic characteristics were described for the first time. In addition, a prediction model for expected trend was successfully constructed.

Epidemic curve and semi-logarithmic line graph consistently illustrated a change in phases of the epidemic which occurred at the beginning of March 2020 with the number of reported cases increasing substantially afterwards. The sudden increase in confirmed COVID-19 cases was a consequence of mass gathering for various events such as Sri Petaling tabligh (Muslim religious gathering) which triggered cluster outbreaks in Malaysia [4]. This indicated that COVID-19 was entering a rapid transmission phase. WHO classified 5 countries in Southeast Asia (Indonesia, Malaysia, Singapore, Thailand and Vietnam) as countries with local transmission on 2 March 2020 [5], and later declared the outbreak a pandemic on 11 March 2020 [2].

Epidemics typically follow the law of exponential growth during their early stages, especially for infectious diseases with a basic reproduction number (R₀)>1.0. Basic reproduction number (R₀) for COVID-19 was estimated to be approximately 2.2 in a previous study focusing on early transmission dynamics in Wuhan, China [6]. In our study, a similar trend of exponential growth was observed and applied in the prediction of a short-term trend for COVID-19. The goodness-of-fit (adjusted R²) of the prediction model for Southeast Asia within the second phase of transmission was 0.96. Our model predicted that the cumulative COVID-19 cases in Southeast Asia region would exceed 10,000 by early April 2020. In fact, the cumulative number of COVID-19 patients in Southeast Asia was 10,153 as of 1 April 2020, surpassing 10,000 for the first time. This actual figure well validates our prediction.

The geo-temporal map displayed a spatial distribution of confirmed cases which presented a condition similar to that of "cancer metastasis". Most of the confirmed COVID-19 cases were concentrated within several international metropolitan areas before spreading to other small to medium-sized cities. Several studies have demonstrated the link between transportation and spread of COVID-19 infections [7, 8]. This seems to explain why metropolises which are major international transportation hubs were more vulnerable. According to the theory of “Iceberg Phenomenon”, identification of infected individuals living in relatively underdeveloped and remote cities may be partially due to improved access to laboratory testing for COVID-19.

We were not able to directly compare distribution of age or sex within patient groups from different countries or regions because of the huge differences in population structure. However, we established that the median age of locally infected COVID-19 patients was significantly higher than that of the general population in respective countries, indicating that age may be a significant risk factor for COVID-19. This reinforces the previous finding that COVID-19 seems to be uncommon in children [9, 10]. In terms of sex, the reported proportion of males in confirmed COVID-19 cases in China, Italy and South Korea was 51.4%, 59.8% and 37.7%, respectively [11-13]. Although more than half of the confirmed cases in Southeast Asia were male, the link between sex and susceptibility for COVID-19 could not be supported by our result of corresponding hypothesis test. In addition, participation in social activities may be considered an intermediary factor [9, 13]. Strikingly, the age and sex composition of confirmed cases in Vietnam reveals a particular degree of “uniqueness” with many more young and female individuals getting infected with COVID-19. This may be attributed to the population structure and the role of women in the Vietnamese society. Nearly 20% of confirmed COVID-19 cases in Southeast Asia included foreign nationals, but this proportion varied from one country to another. Diversity in the composition of nationality among patients may suggest to some extent the risk of virus inputs. As an active center of global community, frequent cross-border population movements increased human-to-human transmission [14]. Consequently, there is need to give serious considerations especially to the rapid spread of the epidemic outside China.

The global crude fatality rate for COVID-19 was 3.9% (6,606 deaths out of 167,515 confirmed cases) as of 16 March 2020 (the end of our study period) [15]. Crude fatality rate for Southeast Asia was largely underestimated in this study because of delayed diagnosis and lack of transparency in the information given by health authorities. For example, the number of deaths from COVID-19 in Indonesia jumped from 5 on 16 March 2020 to 19 on 18 March 2020, thus increasing crude fatality rate to 8.4% [16]. Although most COVID-19 patients may exhibit mild clinical symptoms, older people and individuals with underlying medical conditions may be at higher risk of suffering severe illness and death [11, 13]. Results of our study are consistent with this finding. Median age of the 18 deaths included in the study was significantly higher than that of surviving cases. Among COVID-19 patients who died, 72.2% had underlying conditions, such as diabetes and chronic cardiovascular diseases, among others. Notably, one deceased case from Thailand also had dengue fever [17], a tropical disease that is common and active in Southeast Asia. In fact, it is difficult to distinguish these two viral diseases since they share some clinical and laboratory features [18]. Public health security in this region is facing unprecedented challenges.

The present study has several limitations despite our efforts to ensure data quality and analytical rigor. First, raw data were compiled from publicly available information which is not uniformly distributed. Therefore, the data available for overall analysis and sample size for demographic analysis were limited. Moreover, due to delay in diagnosis of COVID-19 infections and lack of transparency in provided information, undiagnosed and all death cases may have not been comprehensively reported during the early phase of transmission causing an underestimation of the true severity of the outbreak in this region. Third, a combination of various health policies and opportunistic factors makes it difficult to predict trend of the pandemic. We only used a simple and practical model to predict a short-term trend of COVID-19 infections in this study.

SARS-CoV-2

severe acute respiratory syndrome coronavirus 2

COVID-19

coronavirus disease 2019

WHO

the World Health Organization

SE Asia

Southeast Asia

ANOVA

analysis of variance

general population

PWCC

population with confirmed COVID-19 cases

Ethics approval and consent to participate

All data in this study were derived from the information publicly disclosed by officials and involved no individual privacy; hence, the need for ethics approval was waived by the Ethics Committee of The First Affiliated Hospital of Medical College, Zhejiang University.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by the National Natural Science Foundation of China [81790631, 81570512] and the National Key Research and Development Program of China [2018YFC2000500]. However, all of the funders had no role in the design, analysis, preparation of the manuscript, or decision to publish.

Authors' contributions

MZ and LL had the original idea and developed the study protocol. MZ, KJ, SPC, JWHT and NL were involved with the data collection and collation. MZ, JS, KJ and LL performed the analysis and drafted the initial manuscript. All authors contributed in the preparation of manuscript writing, and approved its final version.

Acknowledgements

Not applicable.

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Version 1

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Early Epidemiological Features and Trends of the COVID-19 Outbreak in Southeast Asia: a Population-Level Observational Study

Status:

Version 1

Abstract

Figures

Background

Methods

Data Compilation

Statistical Analysis

Ethical Approval

Results

Temporal Distribution

Spatial Distribution

Demographic Characteristics

Discussion

Abbreviations

Declarations

References

Status:

Version 1