COVID-19: the Epidemiological Hotspot and the Disease ...

Mal J Med Health Sci 17(SUPP8): 42-50, Oct 2021 42

Malaysian Journal of Medicine and Health Sciences (eISSN 2636-9346)

original ARTICLE

COVID-19: the Epidemiological Hotspot and the Disease Spread in Malaysia

Sharifah Norkhadijah Syed Ismail1,2,3, Emilia Zainal Abidin1, Irniza Rasdi1, Nor Eliani Ezani1, Nazri Che Dom4, Aida Soraya Shamsuddin5

1 Department of Environmental and Occupational Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia (UPM), 43400 Serdang, Selangor, Malaysia

2 Research Associate (INTERIM) Malaysian Research Institute on Ageing (MyAgeing), Universiti Putra Malaysia (UPM), 43400 Serdang, Selangor, Malaysia

3 Institute for Social Science Studies, Universiti Putra Malaysia (UPM), Putra InfoPort, 43400 Serdang, Selangor, Malaysia 4 Faculty of Health Sciences, Universiti Teknologi MARA (UiTM), 42300 Puncak Alam, Selangor, Malaysia 5 Department of Nutrition Sciences, Kulliyyah of Allied Health Sciences, International Islamic University Malaysia (IIUM),

25200 Kuantan, Pahang, Malaysia

ABSTRACT

Introduction: This paper focuses on the epidemiological hotspot of COVID-19 cases in Malaysia and the population incidence rates under Movement Control Orders (MCOs). Methods: Dataset from the Ministry of Health Malaysia (MOH) were employed to determine the cumulative incidence rates by using population-based reference data from confirmed infections (cases/10,000 population) and the mapping was done by geographical information systems (GIS) software for three phases of MCOs (17th March – 28th April 2020) in Peninsular Malaysia. Results: The total number of COVID-19 cases reported by MCOs for 42 days was 4,580 and the incidence rate was 17.72 per 100,000 population. The trend of daily new COVID-19 cases reported across the MCOs was 1,949 cases in the first 14 days of the epidemic (MCO1) (the incidence rate of 7.54 per 100,000 population), 1,930 cases during MCO2 (incidence rate of 7.47 per 100,000 population) and 701 cases during the MCO3 (incidence rate of 2.71 per 100,000 population). Conclusion: The MCOs had a significant impact on case reduction. GIS is a useful tool in mapping cases distribution patterns and incidence rates during the MCOs that will assist in the decision making, and more importantly, in social mobilization and community responses.

Keywords: Epidemiology, Hotspot, Incidence rate, Outbreak, GIS

Corresponding Author: Sharifah Norkhadijah Syed Ismail, PhDEmail: [email protected]: +603 8947 2643

INTRODUCTION

Coronavirus disease (COVID-19) was first found in 2019 in Wuhan, China and it is an infectious disease (1). Most patients who were infected with the COVID-19 suffered from mild to severe respiratory diseases and recovered without special treatment (2). Older people and people with pre-existing medical conditions such as diabetes, cardiovascular diseases, chronic respiratory disease, and cancer are moreprone to acquire serious illnesses and are at a higher risk of complications and mortality (2).

As of 19th April, 2,328,179 confirmed cases COVID-19 were reported around the world, 160,516 confirmed deaths and 184 countries were reported with cases (3). The United States of America (US) was the highest reported confirmed cases (734,552 cases) followed by Spain (194.416 cases), Italy (175,925 cases), and France (152,978 cases) (2). The highest number of deaths was reported in the US with 38,835 (2). As of 19th April 2020, 5,389 cases were reported in Malaysia with the active cases of infection were 2,103 cases with 46 cases are receiving treatment in intensive care units (ICU), and 26 cases are on ventilation support (3). As of 11th May 2020, the cumulative number of death due to COVID-19 in Malaysia is 109 cases (1.62% of the total case) (3).

According to the Centers for Disease Control and Prevention (CDC), the virus that causes COVID-19

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Mal J Med Health Sci 17(SUPP8): 42-50, Oct 2021

(SARS-CoV-2) is transmitted primarily through exposure to respiratory fluids containing the infectious virus. There are three main ways to become infected: (1) inhalation of very fine respiratory droplets or aerosol particles, (2) deposition of respiratory droplets and particles on exposed mucous membranes in the mouth, nose, or eye by direct splashes and sprays, and (3) touching mucous membranes with hands that have been soiled either directly by virus-containing respiratory fluids or indirectly by touching surfaces with the virus on them (updated on 7 May 2021) (4). As a result of exhalation such as quiet breathing, speaking, singing, exercise, coughing, and sneezing, people release respiratory fluids in the form of droplets of various sizes (5-11). These droplets carry the virus and spread the infection to others. It is consistent, strong evidence that the SARS-CoV-2 spreads by airborne transmissionis more likely to be dominant (12).

Under the Infectious Disease Prevention and Control Act (1988) Act (PCID Act 1988) and Police Act 1967 (13), Malaysia has announced a Movement Control Order (MCO) to be imposed on a nationwide basis from the 18th to 30th of March 2020. The MCO was announced in an attempt to control and reduce the spread of COVID-19 in Malaysia as there was a sudden spike of cases reported arising from a large cluster of Islamic religious gatherings comprising of estimated 12,000 people in Sri Petaling, Kuala Lumpur, Malaysia on 28th of February to 2nd of March 2020.

Under the PCID Act 1988, the MCO was prescribed by the Prime Minister of Malaysia on 16th March 2020. The MCO was gazetted on 18th March 2020 in the Declaration of Infected Local Areas Order (2020) (PCID Order) where all 14 states of Malaysia were declared as infected local areas. As a tool to guide the implementation and management of the MCO, a regulation was issued under Measures within Infected Local Areas Regulations (2020) (PCID Regulations) where it gazetted the need for the following: a) control of movement and gatherings of persons within the local infected area; b) making mandatory health examinations of a person arriving in Malaysia; c) restrictions on operating hours of entities defined as essential services and d) stipulations of penalties for non-compliance of the PCID Regulations (13).

The MCO implemented in Malaysia follows the largely similar initiative on movement restrictions structure imposed in other countries globally such as China, India, United Kingdom, France, Spain, Italy many other countries. However, implementations of movement restrictions vary to a wide degree with some countries referring to it as a lockdown. As evidence emerged, some countries were found to be more successful in its implementation by way of reduction in the number of cases being reported by their national tally. For example, China has within an average of 90 days been able to

reduce the exponential spike of cases reported in the national tally (14).

Geographic information system (GIS) has been widely used as a real-time mapping of disease cases and disease spread (15). GIS has become an essential tool in analyzing and visualizing the spread of COVID-19 in the process to curb the disease and in improving the quality of care (16). GIS is a useful tool for COVID-19 studies especially in visualizing the community involvement and social mobilization as well as in assisting the relevant agencies in their decision-making process. Understanding the geographical dynamics of COVID-19 is vital to control the spread of the disease. This is due, in large part, to the fact that governmental institutions, international organizations, and private companies have made vast amounts of geospatial data available to the public globally (17). Mollalo et al. (15) applied GIS in their study to evaluate county-level differences in COVID-19 disease incidence throughout the continental United States. The use of GIS to map the effects of significant explanatory variables such as income inequality, median household income, the proportion of black females, and the proportion of nurse practitioners on the spatial distribution of COVID-19 incidence rates can provide the policymakers with helpful data for targeted interventions. In Andersen et al.’s (18) study, GIS has been utilized to understand the spatial determinants of the pandemic across the United Statesby comparing the socioeconomic factors to the case and the death data. The study concludes the factors associated with community-level vulnerability included age, disability, language, race, occupation, and urban status.

This study aims to assess the distribution of COVID-19 cases and the incidence rates by the population in Malaysia during several phases of MCO using GIS. This paper addressed thefollowing research questions: (1) How are the cases distributed across the country? (2) How are the incidence rates of COVID-19 distributed throughout the country during the MCOs? (3) How does MCO influence the changes in the pattern of the case distribution and the incidence rate? To the best of our knowledge, this is the first study to employ GIS to analyze the geographical distribution of COVID-10 across the states and districts of Malaysia during the MCOs. The outcome of this paper is crucial and is expected to help researchers and other stakeholders to understand the pattern of the disease spread in the region and to implement an appropriate intervention program to control the outbreak.

METHODS AND MATERIALS

Study areaMalaysia is a Southern Asia country and composed of two non-contiguous regions: West Malaysia (Peninsular

44Mal J Med Health Sci 17(SUPP8): 42-50, Oct 2021

Malaysia) and East Malaysia (island of Borneo). Peninsular Malaysia covers 132,265 km2, accounting for roughly 40% of the country’s total area. It consists of 11 states and two federal territories. It divided into four main regions; the Northern Region (i.e. Perlis, Kedah, Penang, Perak), East Coast Region (i.e. Kelantan, Terengganu, Pahang), Central Region (Selangor, federal territories of Kuala Lumpur and Putrajaya) and Southern Region (i.e. Negeri Sembilan, Malacca, Johor). For reporting purposes, this paper reports cases according to the four main regions of Peninsular Malaysia. The total population of Malaysia is 25.8 million (19). The largest population is recorded in the Central Region (i.e. Selangor, Kuala Lumpur, and Putrajaya) followed by the South and the East Coast Region (Fig. 1).

Fig 1: Population size by states in Peninsular Malaysia

Data analysisSurveillance cases data compiled by the Crisis Preparedness and Response Centre (CPRC) from 18th

March to 28th April 2020 and which was officially reported by the MOH were used to estimate the cumulated incidence rates using reference population data with confirmed infections (cases/10,000 pop). These data are for 41 days of movement control order (MCO) in Malaysia. There are four phases of MCOs: phase 1 (MCO1: 18th March to 31st March 2020); phase 2 (MCO2: 1st April to 14th April 2020); phase 3 (MCO3:15th April to 28th April) and phase 4 (MCO4: 29th April to 12th May). This study only includes data for the first three phases of the MCO. Data included the number of new cases and cumulative cases reported for each state in Peninsular Malaysia.

The incidence rates were calculated as the sum of all-new episodes of COVID-19 by the MCOs from 18th

March to 28th April 2020 divided by the size of the population. Data on the size of the population was obtained from the 2019 National census. The size of the population was defined as the total population in

a year in person-years. Incidence rates were calculated per 10,000 persons.

Spatial distribution patternThe ArcGIS 10.2 software was used for mapping the distribution pattern of cumulative cases and COVID-19 incidence rates for each state and district during the MCOs. The COVID-19 data and its incidence rate were classified into several categories by using the classification method provided in the ArcGIS. Mapping disease includes mapping point locations of cases, incidence rates by area and standardized rates were computed within the ArcGIS.

RESULTS

The incidence rate of COVID-19 by states From 18th March to 28th April 2020, the total COVID-19 cases reported for Peninsular Malaysia was 4,580, with an incidence rate of 17.72 per 100,000 population. The hotspot area reported with the largest number of cumulative cases were mainly in the Central region where Selangor (1,259 cases) and Kuala Lumpur (1,110 cases) lead the group. The Southern region recorded less than 1,000 cases in Johor (616 cases), Negeri Sembilan (394 cases) and Melaka (179 cases). Meanwhile, other regions reported less than 200 cases. The highest incidence rate was reported in Putrajaya (8.68 per 10,000 population) followed by Kuala Lumpur (6.25 per 10,000 population) and Negeri Sembilan (3.47 per 10,000 population) (Fig. 2). The incidence rate for

Fig 2: The cumulative number of COVID-19 cases (a) and incidence rate per 10,000 population by states (b)



Selangor is 1.92 per 10,000 population and within a similar range to Melaka (1.91), Johor (1.63) and Pahang (1.61). Other states reported less than 1 incidence rate per 10,000 population.

The comparison of incidence rate by MCOs In the first 14 days of MCO 1, there were 1,949 cases of COVID-19 (incidence rate (IR) of 7.54 per 100,000 population), and 1,930 cases of COVID-19 in MCO2 (IR 7.47 per 100,000 population) followed by 701 cases during MCO3 (IR 2.71 per 100,000 population) (Fig. 3).

Fig 3: The reported daily COVID-19 cases by MCOs

From the GIS-based maps, the MCOs produced a positive outcome in controlling the frequency of new cases (Fig. 4) and the incidence rate of COVID-19 (Fig.

Fig 4: The total number of newly reported cases by MCOs

5) by states. The number of cases and incidence rates reduced significantly during MCO3. The distribution of Covid-19 cases in most districts showed a significant reduction by the MCOs where less than 50 cases were reported except for some part of the peninsular including

Fig 5: The incidence rate (per 10,000 population) by states from MCO 1 to MCO 3

Fig 6: The cases distribution by district from MCO 1 to MCO 3

the hotspot area at the central, south, and the east coast region (see Fig. 6). However, the incidence rate by districts shows an increasing trend from MCO 1 to MCO 3 especially in the region with high reported cases (Fig. 7). This was influenced by the increase in the number of cases and the population size of the district. The cumulative incidence of COVID-19 ranged significantly in each district, from 0.14 cases per 10,000 population in Sik, Kedah to 12.55 cases per 10,000 in Rembau, Negeri Sembilan. There was an absolute change in the cumulative incidences in most areas during MCO 3.


in the central region of Peninsular Malaysia. The three main clusters in this area were reported with a total of 276 cases (3).

In comparison to Singapore, there are a total of 28,038 confirmed cases, with 9,340 discharged and 22 deaths (as of 17th May 2020). The cases were drawn as clusters of visited several places including religious institutions, workplaces, retail businesses, private events, hospitals and schools (23). The imported cases have also contributed to the outbreak, with the majority of new cases occurring in foreign worker dorms and construction sites, which are 23,008 cases of the 25,346 confirmed cases (23).

The major means of the outbreak in Malaysia is through transmission from person to person among close contacts. This also has been reported in several studies where clusters of cases were reported following family, work, or social gatherings (24). In China, there were 1183 case clusters, 88% of which contained two to four confirmed cases reported in 20 provinces outside of Hubei which 64% of clusters were documented within familial households (25).

For the past 42 days of the MCO, the highest incidence rate was reported in the central region includes the area of Putrajaya (8.68 per 10,000 population), Kuala Lumpur (6.25 per 10,000 population) and Negeri Sembilan. A similar trend was reported among EU/EEA countries where the incidences of reported COVID-19 cases are high heterogeneity, especially within countries with densely populated areas. This has resulted in a significantly higher prevalence than in other areas of the same country (26). According to the CDC, the high incidence rate was connected to population density in specific areas, the timing and intensity of mitigation actions, diagnostic tests, the timing of COVID-19 introductions, and the patients’ underlying medical conditions (27).

In comparison with countries such as China, Italy, and Iran, the incidence rate of Covid-19 over the first 22 days has grown dramatically, reaching 1.81 cases per million people. Over the next 11 days, the incidence rate decreased to 0.07 cases per million people. Nevertheless, the incidence rate rose significantly to 1.43 cases per million people, with varying percentages of cured and mortality cases throughout the countries. Nonetheless, the total mortality rate has been decreasing since the outbreak began (28).

In contrast, the prevalence of COVID-19 incidence in 14-days reported in EU/EEA was estimated at 82.1 per 100,000 population. It was ranged from 5cases (in Bulgaria) to more than 100 cases per 100,000 population in Belgium (149.5), Iceland (275.5), Italy (113.6), Luxembourg (323.8), United Kingdom (117) and Spain (218.2) (24). The overall COVID-19 prevalence in

DISCUSSION

This study explored the distribution of COVID-19 cases in Malaysia during several phases of MCO. The results of the present study indicate that the hotspot area has been identified within the central region of the country (Selangor and Kuala Lumpur). This was related to the large cases reported arising from a large cluster of Islamic religious gatherings in Sri Petaling, Kuala Lumpur, Malaysia.

The religious gathering was reported to comprise an estimated 12,000 participants in some reports and up to 16,000 people in other media outlets. An estimated 1,500 of the participants were of international origins from 25 different countries including China, South Korea, Canada, Nigeria, India, and Australia (20, 21). The officials in Malaysia were initially placed on high alert for an impending risk of a large COVID-19 cluster related to the religious gathering after Brunei reported its first case on the 9th of March. Subsequently, the officials in Malaysia requested for participants of the gathering to come forward for testing of the virus to isolate those who are positive and quarantine those with the risk of exposure and their close contacts.

A total of 13,762 participants of the religious gathering in the Sri Petaling were screened, with 9,327 samples were tested. This is the largest COVID-19 cluster in Malaysia with a total of 2,345 cases or 35% of the total (3). Four sub-clusters of infections have been identified by The MOH in many religious schools that have had close contact with infected participants of the Sri Petaling gathering (22). As of 12th May, out of 32 COVID-19 clusters in Malaysia, there were 14 clusters reported

Fig 7: The incidence rate (per 10,000 population) by district from MCO 1 to MCO 3



the United States varies greatly by jurisdiction, ranging from 20.6 cases per 100,000 population in Minnesota to 915.3 in New York City(27). Nine jurisdictions were reported with the cumulative cases above the national rate; New York City (915.3), New York (555.5), New Jersey (498.6), Louisiana (349.4), Massachusetts (220.3), Connecticut (217.8), Michigan (189.8), District of Columbia (172.4), and Rhode Island (133.7) (27).

The GIS map has illustrated the influence of the MCOs on the trend of the cases and their distribution. The map showed a reduction pattern in terms of the number of cases and the incidence rate. One of the reasons for the success of MCOs in controlling the spread of the cases in Malaysia is through the targeted approach of identifying COVID-19 cases. This is where active case detection or contact tracing are performed especially at specific localities where cases of COVID-19 have been reported. Active case detection is when health services staff actively tests the community to identify positive cases to isolate them and their close contact to quarantine them. Active case detection has been performed at localities that were imposed with the Enhanced MCO (EMCO). In these localities, spikes of cases are usually detected because community testing for COVID-19 is performed to seek out positive cases for subsequent treatment in the hospital. The mapping of COVID-19 cases in this study was not able to differentiate between cases reported via active case detection in localities under EMCO or other cases such as community infection or import cases.

The contact tracing approach seems to be suitable to detect active COVID-19 cases for now since 84.9% of the positive COVID-19 among religious schools are those with no symptoms (3). This has made the effort to break the COVID-19 transmission chain more difficult than envisaged. Several studies have reported the secondary attack rate among household contact was up to 16% than most of the secondary infections in the early stages of epidemics at various locations. In China, for example, 1-5% of tens to thousands of close contacts were secondary COVID-19 infections. Most were within the family, with a secondary infection in the household ranging from 3 to 10%. In the United States, the secondary infection was 0.45 % among close contacts (445 close contacts of 10 confirmed cases), and 10.5% among the household members (10). Meanwhile, in Korea, the secondary infection was 0.55 % among close contacts and 7.6 % among family members (29).

Even though the MCOs demonstrates a significant decrease in the incidence rate of COVID-19, however, the degree of transmissibility or infectiousness of the COVID-19 (infectivity) has raised concerns among researchers, health care providers, politicians, and the general public. This is termed as the R0 (R naught) or defined as “basic reproduction number”. R0 denotes the predicted number of cases directly infected by a single infectious case in a community where everyone

is presumed to be susceptible to infection (30). For example, if the value of R0 is two, this indicates that each positive COVID-19 case can spread the disease to two others. Lowering the R-value to less than one helps to end an outbreak whereas an epidemic will grow exponentially for an R above one (30).

Before the MCO was implemented, the R0 was calculated as 3.55 after the sudden increase in cases from the religious gathering at the Sri Petaling Mosque. This is higher than the index for viral pathogens in MERS and SARS epidemics, which was estimated to be two. The cautious approach taken by Malaysia has shown that the MCO 1, 2, and 3 and the EMCO have been able to reduce the R0 to 0.3 through various preventive measures and public health controls. The R0 calculated in this study before the MCO was relatively higher than the index value dataestimated in Wuhan, China (2.2: 95% CI, 1.4-3.9) (8). This has shown that the infectivity of COVID-19 in Malaysia was higher than in the country where the epidemics originated (31). Other studies also reported varies range of R0 where the lowest of which was reported by the WHO (1.95:95% CI 1.4-2.5) (32) and the highest was 6.47 (95% CI 5.71-7.23) (33). Liu et al. (6) also confirmed that the average R0 for COVID-19 was 3.28 with a median of 2.79 and an Interquartile Range (IQR) of 1.16 (34). A variation of the calculated R0 rate is due to diverse calculation approachesthat were used and done at different periods of the outbreak.

The trend of COVID-19 infection indicated an early concentration of infections in urbanized areas around the world, and Malaysia was no exception. The findings of this study revealed that highly urbanized states with the highest population density in Malaysia had the highest cumulative incidence of COVID-19 such as in Selangor and Kuala Lumpur. The highest incidence rates were observed in Putrajaya, a planned city and the federal administrative center of Malaysia. A similar trend was observed for Kuala Lumpur, the country’s largest city and ranked among Southeast Asia’s fastest-growing metropolitan regions both in terms of population and economic growth. As a result of mobility, intermixing, and risk-taking, city life is more susceptible to COVID-19 transmission than a rural area, particularly in developing countries (35). Increased population density, public transportation use, and indoor work environments were all factors that contributed to the spread of this pandemic in urban areas (35). Increased indoor activity and higher occupant density in the urban areas increased human-to-human contact.

Furthermore, our result showed that the COVID-19 incidence rate during the MCOs was higher in the rural district (e.g. Rembau district with IR 12.55 per 10,000 population) as compared to the urban districts. The lowest incidence rate also was recorded in the rural district, Sik, Kedah. There is debate concerning the implications of COVID-19 exposure and infections,


particularly in rural areas. For instance, Karim and Chen (36) indicate that rural areas are more vulnerable than urban areas based on the assertion that rural areas have a greater percentage of elderly people and a higher prevalence of chronic diseases than urban areas, making rural populations more vulnerable during the COVID-19 pandemic. However, sparsely populated and the natural geography location encourages social distancing and isolationis believed to be the factors that protect rural residents in lowering both the rate of exposure and COVID-19 virus contractions, resulting in fewer confirmed COVID-10 cases (36).

The pandemic in Malaysia appeared to be a complex aggregation of several individual outbreaks that occurred at different time points in different geographic locations in the country, owing to the varying population density, influenced by mobility patterns and the available healthcare system, which greatly influenced the varying trajectories across the states in response of the effect of the nationwide lockdown. While many urban areas are already seeing an increase in COVID-19 cases, the slower progression of cases in rural areas may make the COVID-19 pandemic look a distant reality. The lower incidence of confirmed COVID-19 cases in rural areas should not be interpreted as a reprieve from the COVID-19 pandemic, but rather as the calm before the storm. Rural areas, while delayed, would most certainly suffer the full intensity of the COVID-19 pandemic, with potentially worse short- and long-term effects than urban areas. Furthermore, there was the risk of inadequate surveillance in rural areas, which might have gone undetected. CONCLUSION

The incidence mappings illustrate and provide a common frame of reference and work as an aid for better visualization to understand the COVID-19 disease pattern and to provide response planning for the authority during the outbreak. GIS provides location intelligence real-time insights about the impact of this disease on the country by its region, to the businesses, and other activities. This helps the authority in their planning to increase the overall resilience leaving organizations better prepared for the response. The cautious approaches through the MCOs have been able to reduce the cases. This indicates Malaysians must continue to adhere to the guidelines and the Standard Operating Procedures (SOPs) introduced to maintain and break the COVID-19 infection chain in the country.

ACKNOWLEDGEMENTS

We would like to thank to the Ministry of Health Malaysia (MOH) for the datasets which are available at https://covid-19.moh.gov.my/.

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