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International Journal of
Environmental Research
and Public Health
Article
Spatial and Temporal Analysis of Plasmodium knowlesi
Infection in Peninsular Malaysia, 2011 to 2018
Wei Kit Phang 1, Mohd Hafizi Abdul Hamid 2, Jenarun Jelip 2, Rose Nani Mudin 2,
Ting-Wu Chuang 3,* , Yee Ling Lau 1and Mun Yik Fong 1
1Department of Parasitology, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia;
weikitphang@gmail.com (W.K.P.); lauyeeling@um.edu.my (Y.L.L.); fongmy@um.edu.my (M.Y.F.)
2Disease Control Division, Ministry of Health Malaysia, Putrajaya 62000, Malaysia;
dr.mhafizi@moh.gov.my (M.H.A.H.); jenarun@moh.gov.my (J.J.); drrose@moh.gov.my (R.N.M.)
3Department of Molecular Parasitology and Tropical Diseases, School of Medicine, College of Medicine,
Taipei Medical University, Taipei 11031, Taiwan
*Correspondence: chtingwu@tmu.edu.tw; Tel.: +886-2-27361661
Received: 20 October 2020; Accepted: 8 December 2020; Published: 11 December 2020
Abstract:
The life-threatening zoonotic malaria cases caused by Plasmodium knowlesi in Malaysia has
recently been reported to be the highest among all malaria cases; however, previous studies have mainly
focused on the transmission of P. knowlesi in Malaysian Borneo (East Malaysia). This study aimed to
describe the transmission patterns of P. knowlesi infection in Peninsular Malaysia (West Malaysia).
The spatial distribution of P. knowlesi was mapped across Peninsular Malaysia using Geographic
Information System techniques. Local indicators of spatial associations were used to evaluate spatial
patterns of P. knowlesi incidence. Seasonal autoregressive integrated moving average models were
utilized to analyze the monthly incidence of knowlesi malaria in the hotspot region from 2012 to 2017
and to forecast subsequent incidence in 2018. Spatial analysis revealed that hotspots were clustered
in the central-northern region of Peninsular Malaysia. Time series analysis revealed the strong
seasonality of transmission from January to March. This study provides fundamental information on
the spatial distribution and temporal dynamic of P. knowlesi in Peninsular Malaysia from 2011 to 2018.
Current control policy should consider different strategies to prevent the transmission of both human
and zoonotic malaria, particularly in the hotspot region, to ensure a successful elimination of malaria
in the future.
Keywords:
spatial analysis; time series analysis; demography; malaria; Plasmodium knowlesi;
Peninsular Malaysia
1. Introduction
Over the decades, malaria has persisted as one of the major vector-borne parasitic diseases
globally. Its impacts are geographically variable, depending on the intensity of transmission and
the parasite species involved [
1
]. According to the World Health Organization report, an estimated
228 million malaria cases were reported in at least 80 countries and territories in 2018 [
2
]. With the
effort of the roll back malaria initiative, there has been a significant progress in reducing malaria
morbidity and mortality through strategies such as comprehensive disease surveillance, distribution of
insecticide-treated bednets and the use of highly effective artemisinin combination medications [2].
In Malaysia, the Malaria Eradication Program was introduced in 1967, which continued until
the 80s. The program has resulted in a significant reduction in malaria cases by approximately
94.3% in Peninsular Malaysia [
3
]. The incidence of human malaria from Plasmodium vivax and
Plasmodium falciparum have dropped steadily nationwide. However, the emergence of zoonotic malaria
Int. J. Environ. Res. Public Health 2020,17, 9271; doi:10.3390/ijerph17249271 www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2020,17, 9271 2 of 21
caused by Plasmodium knowlesi has become the main cause of clinical malaria in Malaysia [
1
]. This has
certainly challenged Malaysia’s efforts to eliminate malaria.
P. knowlesi is a simian malaria parasite which is prevalent in Southeast Asia and gaining prominence
for its role in the increasing human malaria cases [
4
]. Its clinical manifestations typically include fever,
chills, headache, malaise, rigors, and anorexia [
5
]. Severe cases can be highly life-threatening. A recent
study reported that the average case fatality rate of P. knowlesi infection in Peninsular Malaysia from
2013 to 2017 was 1.2% [
6
]. It is often misdiagnosed via microscopy as P. malariae infection owing
to morphological similarities between the two species at the trophozoite, schizont, and gametocyte
stages [
7
,
8
]. This limitation warrants the need of molecular diagnostic techniques such as polymerase
chain reaction (PCR), to identify and distinguish P. knowlesi as well as other Plasmodium species
infections by amplification of species–specific gene targets. P. knowlesi is mainly transmitted to human
from non-human primates via bite of forest-dwelling Anopheles mosquitoes from the Leucosphyrus
group [
9
]. However, the possibility of human-to-human transmission has been proposed in a previous
entomological study in Vietnam [10].
Malaria case detection in Peninsular Malaysia utilizes three systems: active case detection (ACD),
mass blood survey (MBS), and passive case detection (PCD). ACD involves screening for febrile
individuals in localities experiencing outbreaks or within high-risk groups such as the military,
indigenous people, rural settlers, and migrant workers. MBS requires at least 80% of the community
members who receive insecticide-treated nets (ITNs) and residual spraying to be tested. Moreover,
MBS is applied as a response during outbreaks when health officers are required to screen every
individual within an outbreak locality. PCD consists of detecting malaria cases among patients visiting
health centers for their treatment. Most confirmed human and simian malaria cases were detected
through the PCD approach.
Understanding the spatial and temporal patterns of P. knowlesi infection is important not only for
case detection but also for resource allocation. Spatial analysis is commonly used for disease hotspot
identification and evaluation of patterns of epidemics. Several studies have used spatial-temporal
analysis to detect the clustering of malaria cases [
11
–
13
]. A better understanding of the spatial
distribution of knowlesi malaria is useful to improve intervention programs and health resource
allocation. In addition, cluster detection can aid in more focused epidemiological surveillance of
macaque reservoir hosts and research on the bionomics of mosquito vectors. Studies based on
spatial-temporal analysis of malaria distribution in Sabah, Malaysian Borneo (East Malaysia) have
been conducted extensively [
14
–
16
]. However, a similar study investigating transmission patterns in
Peninsular Malaysia (West Malaysia) is lacking. Therefore, this study aimed to use spatial-temporal
analysis to identify P. knowlesi infection hotspots in Peninsular Malaysia at the district level between 2011
and 2018. Furthermore, state-wide demographic parameters of knowlesi malaria in Peninsular Malaysia
were also investigated to reveal fundamental epidemiological characteristics of P. knowlesi infections.
2. Materials and Methods
2.1. Geography and Demography of Study Areas
Malaysia is a country in Southeast Asia, and it is separated by the South China Sea into two
regions, Peninsular Malaysia and Malaysian Borneo. The national census in 2010 estimated the total
population of Malaysia at 28.3 million [
17
]. The annual citizen population growth rate was projected at
1.6% in 2010. However, the growth rate declined to 1.1% in 2018.
Our study area primarily focused on Peninsular Malaysia which spreads from latitude 1
◦
15
0
50.0
00
N
to 6
◦
43
0
36
00
N and from longitude 99
◦
35
0
E to 104
◦
36
0
E. Peninsular Malaysia covers a land area of
13.21 million hectares (Figure 1) [
18
]. Forested areas account for approximately 5.76 million hectares,
which is equivalent to 43.6% of the land area of Peninsular Malaysia [
18
]. The climate is categorized as
equatorial, typically hot and humid throughout the year. Rainfall pattern in Peninsular Malaysia is
mainly influenced by the southwest monsoon season (from May to August) and northeast monsoon
Int. J. Environ. Res. Public Health 2020,17, 9271 3 of 21
season (from November to February) [
19
]. The annual rainfall ranges from a maximum of 5000 to a
minimum of 1750 mm. The mean daily temperature ranges from 25 to 28 ◦C.
Int. J. Environ. Res. Public Health 2020, 17, x 3 of 21
monsoon season (from November to February) [19]. The annual rainfall ranges from a maximum of
5000 to a minimum of 1750 mm. The mean daily temperature ranges from 25 to 28 °C.
Figure 1. Peninsular Malaysia map showing states (divided into administrative levels of district) and
federal territories.
Peninsular Malaysia is divided into 11 states (Perlis, Kedah, Pulau Pinang, Perak, Selangor,
Negeri Sembilan, Melaka, Johor, Kelantan, Terengganu, and Pahang) and 2 federal territories (Kuala
Lumpur and Putrajaya) (Figure 1). Approximately 24.7% of the population of Peninsular Malaysia
reside in sub-urban and rural areas.
2.2. Data of Knowlesi Malaria in Peninsular Malaysia
Laboratory diagnosis of knowlesi malaria is conducted via microscopic examination or PCR-
based approaches [20]. At present, District Health Offices notify the State Health Departments of the
confirmed positive malaria cases, which will be further compiled by the Ministry of Health Malaysia.
In this study, laboratory confirmed knowlesi malaria case data for the period 2011–2018 were
provided by the Ministry of Health Malaysia. This dataset contains information on each confirmed
case, including state, district, year, nationality, ethnicity, citizenship, occupation, age, gender, case
classification, and date of onset. The population data of Peninsular Malaysia were obtained from the
Department of Statistics Malaysia open-source platform [17,21]. The descriptive epidemiological
analysis was conducted at the state level to show the demographic characteristics of indigenous P.
knowlesi infection cases. An indigenous malaria case is a case contracted locally with no evidence of
importation and no direct link to transmission from an imported case, whereas an imported malaria
is a case in which the infection was acquired outside the area in which it was diagnosed [22]. The case
Figure 1.
Peninsular Malaysia map showing states (divided into administrative levels of district) and
federal territories.
Peninsular Malaysia is divided into 11 states (Perlis, Kedah, Pulau Pinang, Perak, Selangor,
Negeri Sembilan, Melaka, Johor, Kelantan, Terengganu, and Pahang) and 2 federal territories
(Kuala Lumpur and Putrajaya) (Figure 1). Approximately 24.7% of the population of Peninsular
Malaysia reside in sub-urban and rural areas.
2.2. Data of Knowlesi Malaria in Peninsular Malaysia
Laboratory diagnosis of knowlesi malaria is conducted via microscopic examination or PCR-based
approaches [
20
]. At present, District Health Offices notify the State Health Departments of the confirmed
positive malaria cases, which will be further compiled by the Ministry of Health Malaysia. In this
study, laboratory confirmed knowlesi malaria case data for the period 2011–2018 were provided by the
Ministry of Health Malaysia. This dataset contains information on each confirmed case, including state,
district, year, nationality, ethnicity, citizenship, occupation, age, gender, case classification, and date of
onset. The population data of Peninsular Malaysia were obtained from the Department of Statistics
Malaysia open-source platform [
17
,
21
]. The descriptive epidemiological analysis was conducted
at the state level to show the demographic characteristics of indigenous P. knowlesi infection cases.
An indigenous malaria case is a case contracted locally with no evidence of importation and no direct
Int. J. Environ. Res. Public Health 2020,17, 9271 4 of 21
link to transmission from an imported case, whereas an imported malaria is a case in which the
infection was acquired outside the area in which it was diagnosed [
22
]. The case data are available at
both the state and district levels; however, we aggregated data based on district locations where the
cases were reported for subsequent analysis.
This study was registered with the National Medical Research Register (NMRR-16-2109-32928),
and ethical approval was obtained from the Malaysian Research Ethical Committee (MREC) (reference
no. KKM/NIHSEC/P16-1782 (11)).
2.3. Spatial Analysis of Knowlesi Malaria in Peninsular Malaysia
Spatial analysis was conducted at the district level in Peninsular Malaysia for the period 2011–2018.
The annual square root transformed incidence rate (IR) per million people for each district was
calculated to deal with the highly skewness of raw incidence rate caused by outliers and deviation from
normality of variance. We used the population estimates from the 2010 National Census data [
17
] and
annual state population growth estimation from the Department of Statistics Malaysia [
21
] to calculate
the annual mid-year population for each district from 2011 to 2018, assuming that the population
growth rate of each district was the same as the state’s population growth. The calculated annual
mid-year population was used to estimate annual IRs accurately.
Both Global and Local Moran’s I statistics were performed to identify the spatial autocorrelation
and disease hotspots in the study area. First order queen contiguity spatial weight matrix was
generated to define districts with shared borders and vertices as neighbors for subsequent analysis.
Global Moran’s I test can be used to detect the spatial autocorrelation of P. knowlesi infections. It provides
a continuous index value, which can be an indication of random distribution (value of zero or close to
zero), clustered distribution (value close to +1.0), or dispersed but organized distribution (value close
to −1.0) [23]. The equation for Global Moran’s I is as follows:
I=nPn
i=1Pn
j=1wij(xi−x)xj−x
(Pn
i=1Pn
j=1wij)Pn
i=1(xi−x)2(1)
where nis the number of observations,
x
is the mean of the variable, x
i
is the variable value at a
particular location, x
j
is the variable value at another location, w
ij
is a weight indexing location of i
relative to j.
Local Moran’s I test involves the formation of Local Indicators of Spatial Association (LISA)
statistics to detect statistically significant spatial clusters of a disease (hot spots and cold spots) as well
as to identify outliers. The equation for Local Moran I statistics is as follows:
Ii=ziX
j
wijzj, (2)
with ziand zjare in deviations from the mean.
LISA values present four types of clusters: high-high, low-low, high-low, and low-high [
24
].
High-high clusters are associated with high IR areas (hot spots), whereas low-low clusters are associated
with low IR areas (cold spots). High-low and low-high categories represent outliers. The statistic
inferences of both tests were performed by Monte Carlo simulation with 99,999 permutations to
generate the p-value. False Discovery Rate (FDR) correction was applied to reduce alpha risk
(Type I error), and resulted in p-value =0.008 being identified as the significance cut-off. Global and
Local Moran’s I tests were performed using GeoDa 1.14 (University of Chicago, Chicago, IL, USA).
Data visualization was performed with QGIS 3.6.3 (Open Source Geospatial Foundation, Beaverton,
OR, USA) to demonstrate the spatial and temporal patterns of P. knowlesi in Peninsular Malaysia from
2011 to 2018.
Int. J. Environ. Res. Public Health 2020,17, 9271 5 of 21
2.4. Time Series Analysis of P. knowlesi Incidence in the Hotspot Region
Knowlesi malaria cases in all districts identified as hotspots were summed into a single dataset.
The cases were aggregated by month to calculate the monthly IR from January 2012 to December 2018.
Cases reported in 2011 were not included as the onset date information was not recorded. Trend and
seasonality of the monthly IR was identified using stl() function in R version 3.5.3 (R Foundation for
Statistical Computing, Vienna, Austria). Time series data of monthly IR was decomposed into trend
and seasonality components using locally estimated scatterplot smoothing [25].
The dataset was assigned into a training period (January 2012 to December 2017) and a validation
period (January 2018 to December 2018). The training period data were used to create and fit seasonal
autoregressive integrated moving average (SARIMA) models whereas the validation period data were
used to test the model forecasting performance. SARIMA forecasting procedure was conducted in
R version 3.5.3 (R Foundation for Statistical Computing, Vienna, Austria) by utilizing fUnitRoots,
forecast, and tseries packages. SARIMA model is generally denoted as SARIMA (p, d, q) (P, D, Q)
m
,
which can be distinguished into non-seasonal components (p, d, and q) and seasonal components
(
P, D, Q
, and m). In non-seasonal component notation, parameter p is the order of autoregressive (AR),
d is the order of differencing, and q is the order of moving average (MA). Parameters P, D, and Q are
the orders of AR, differencing, and MA, respectively, whereas m refers to the number of periods in
each season, defined as 12 in this study.
The monthly knowlesi malaria incidence rates were corrected to achieve stationarity via
differencing approach. Stationarity of the time series was tested using augmented Dickey–Fuller test
and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) unit root test. Order of differencing required was
identified from the minimum number of differencing required to achieve stationarity. Orders of AR
and MA terms were determined based on autocorrelation function (ACF) and partial autocorrelation
function (PACF) plots of the differenced series (Figure A3). R functions, auto.arima() and arima(),
were used for automatic parameters selection based on the Akaike information criterion (AIC),
Bayesian information criterion (BIC), root mean square error (RMSE), and mean absolute percentage
error (MAPE). Lower values of these diagnostic statistics indicate better model performances and
prediction accuracy [
26
]. For diagnostic checking of the fitted models, the Ljung–Box test was used to
evaluate the model residual patterns. The null hypothesis of the Ljung–Box test is that the residuals
are random. Random residuals indicate the absence of significant temporal autocorrelation across
multiple time lags, which could be displayed in ACF and PACF plots, and the model is not lack of fit.
The models were excluded if the residuals showed sign of non-randomness (p<0.05). The best-fitted
model was used to forecast the IR of knowlesi malaria from January 2018 to December 2018. The model
forecast with 80% and 95% prediction intervals was compared against the validation period dataset
between January 2018 to December 2018.
3. Results
3.1. Demographic Characteristics of Knowlesi Malaria from 2011 to 2018
The demographic characteristics of indigenous P. knowlesi cases in Peninsular Malaysia are shown
in Table 1. From 2011 to 2018, 2587 indigenous P. knowlesi cases were reported in Peninsular Malaysia.
The number of indigenous P. knowlesi cases was apparently higher than other indigenous human
malaria cases (P. malariae,P. falciparum, and P. vivax) in most years except 2011 and 2016 (Figure A1).
Although fewer females were infected with P. knowlesi as compared to males, female patients (median
39 years, interquartile range 23–52 years) were generally older than the males (median 34 years,
interquartile range 25.5–45 years). Bimodal age pattern was displayed among female patients with the
highest proportion among the age group of 40–49 years followed by 10–19 years, whereas the mode for
male patients was the 30–39 years age group (Figure 2). Indigenous cases were more prevalent among
the Malays (58.7%) than among other ethnicities. Foreigners accounted for 16.4% of the indigenous
cases. Forest-related occupations accounted for 53.96% of the total indigenous cases. One-third of
Int. J. Environ. Res. Public Health 2020,17, 9271 6 of 21
the total indigenous cases was reported among estate, farm, and plantation workers (Table 1and
Figure A2).
Table 1.
Demographic characteristics of indigenous P. knowlesi cases in Peninsular Malaysia from 2011
to 2018.
Variable N (%)
Age group
0 to 9 53 (2.0)
10 to 19 249 (9.6)
20 to 29 624 (24.1)
30 to 39 707 (27.3)
40 to 49 437 (16.9)
50 to 59 314 (12.1)
60 and above 203 (7.8)
Gender
Male 2167 (83.8)
Female 420 (16.2)
Ethnicity
Chinese 145 (5.6)
Indian 44 (1.7)
Malay 1518 (58.7)
Orang Asli (aborigine community) 372 (14.4)
Sabahan 27 (1.0)
Sarawakian 34 (1.3)
Others 429 (16.6)
No data 18 (0.7)
Occupation class
Agroculture and fishery 20 (0.8)
Army and police * 138 (5.3)
Children and students 224 (8.7)
Construction 98 (3.8)
Ecotourism, forest, and wildlife management * 39 (1.5)
Estate, farm, and plantation workers * 897 (34.7)
Factory workers 55 (2.1)
Forest resource gatherers * 82 (3.2)
Logging * 121 (4.7)
Logistics and transportation 43 (1.7)
Mining * 51 (2.0)
Other manual workers and skilled labors 134 (5.2)
Village labors * 68 (2.6)
Others 365 (14.1)
Unemployed 241 (9.3)
No data 11 (0.4)
Citizenship
Locals 2161 (83.5)
Foreigners 423 (16.4)
No data 3 (0.1)
* Occupations with exposure to the forest.
Int. J. Environ. Res. Public Health 2020,17, 9271 7 of 21
Int. J. Environ. Res. Public Health 2020, 17, x 7 of 21
Figure 2. Proportion of indigenous knowlesi malaria cases by age and gender from 2011 to 2018.
The age and gender distribution of indigenous P. knowlesi cases according to state are
characterized in Tables 2 and 3. P. knowlesi was significantly more prevalent in the population aged
20–29 years in Negeri Sembilan and Pahang, and aged 30–39 years in Johor, Kedah, Kelantan, Perak,
Selangor, and Terengganu (Table 2). The incidence of P. knowlesi was generally higher among males
than among females in most states (Table 3).
Figure 2. Proportion of indigenous knowlesi malaria cases by age and gender from 2011 to 2018.
The age and gender distribution of indigenous P. knowlesi cases according to state are characterized
in Tables 2and 3.P. knowlesi was significantly more prevalent in the population aged 20–29 years
in Negeri Sembilan and Pahang, and aged 30–39 years in Johor, Kedah, Kelantan, Perak, Selangor,
and Terengganu (Table 2). The incidence of P. knowlesi was generally higher among males than among
females in most states (Table 3).
Int. J. Environ. Res. Public Health 2020,17, 9271 8 of 21
Table 2. Age-based distribution of indigenous knowlesi malaria according to states from 2011 to 2018.
Age Group
Number of Indigenous Knowlesi Malaria Monoinfection Cases According to States (%)
Johor Kedah Kelantan Melaka Negeri
Sembilan Pahang Perak Perlis Pulau
Pinang Selangor Terengganu Kuala
Lumpur
0 to 9 0
(0.0)
1
(1.7)
18
(2.7)
0
(0.0)
3
(2.5)
16
(2.4)
14
(2.7)
0
(0.0)
0
(0.0)
1
(0.5)
0
(0.0)
0
(0.0)
10 to 19 4
(2.4)
0
(0.0)
83
(12.2)
1
(14.3)
6
(5.1)
59
(8.9)
60
(11.6)
0
(0.0)
1
(16.7)
24
(11.4)
11
(6.9)
0
(0.0)
20 to 29 47
(28.5)
8
(13.3)
165
(24.3)
2
(28.6)
39
(33.1)
167
(25.2)
107
(20.7)
0
(0.0)
0
(0.0)
55
(26.2)
34
(21.3)
0
(0.0)
30 to 39 58
(35.2)
24
(40.0)
176
(26.0)
1
(14.3)
24
(20.3)
163
(24.6)
137
(26.4)
1
(100.0)
2
(33.3)
66
(31.4)
55
(34.4)
0
(0.0)
40 to 49 33
(20.00)
12
(20.0)
114
(16.8)
1
(14.3)
19
(16.1)
108
(16.3)
93
(18.0)
0
(0.0)
0
(0.0)
29
(13.8)
27
(16.9)
1
(100.0)
50 to 59 17
(10.3)
12
(20.0)
75
(11.1)
1
(14.3)
17
(14.4)
86
(13.0)
66
(12.7)
0
(0.0)
1
(16.7)
20
(9.5)
19
(11.9)
0
(0.0)
60 and above 6
(3.6)
3
(5.0)
47
(6.9)
1
(14.3)
10
(8.5)
64
(9.7)
41
(7.9)
0
(0.0)
2
(33.3)
15
(7.1)
14
(8.8)
0
(0.0)
Total 165
(100.0)
60
(100.0)
678
(100.0)
7
(100.0)
118
(100.0)
663
(100.0)
518
(100.0)
1
(100.0)
6
(100.0)
210
(100.0)
160
(100.0)
1
(100.0)
Int. J. Environ. Res. Public Health 2020,17, 9271 9 of 21
Table 3.
Gender-based distribution of indigenous knowlesi malaria cases according to states from 2011
to 2018.
State
Number of Indigenous Knowlesi
Malaria Cases According to Gender (%) Total (%)
Male Female
Johor 157 (93.15) 8 (4.85) 165 (100.00)
Kedah 56 (93.33) 4 (6.67) 60 (100.00)
Kelantan 576 (84.96) 102 (15.04) 678 (100.00)
Melaka 6 (85.71) 1 (14.29) 7 (100.00)
Negeri Sembilan 98 (83.05) 20 (16.95) 118 (100.00)
Pahang 546 (82.35) 117 (17.65) 663 (100.00)
Perak 414 (79.92) 104 (20.08) 518 (100.00)
Perlis 1 (100.00) 0 (0.00) 1 (100.00)
Pulau Pinang 4 (66.66) 2 (33.33) 6 (100.00)
Selangor 181 (86.19) 29 (13.81) 210 (100.00)
Terengganu 128 (80.00) 32 (20.00) 160 (100.00)
Kuala Lumpur 0 (0.00) 1 (100.00) 1 (100.00)
3.2. State-Level Trend of P. knowlesi Incidence in Peninsular Malaysia from 2011 to 2018
Between 2011 and 2018, a total of 2767 (include imported cases) P. knowlesi monoinfection cases
were reported in Peninsular Malaysia. Of these cases, 812 (29.35%) were from Kelantan state, while 666
(24.07%) and 524 (18.94%) were from Pahang and Perak, respectively (Table 4).
Table 4.
Number of cases of knowlesi malaria in Peninsular Malaysia and by states from 2011 to 2018.
States
Annual Number of Knowlesi Malaria Cases
Total %
2011 2012 2013 2014 2015 2016 2017 2018
Total 277 414 474 332 113 136 423 598 2767 100
Johor 7 14 22 15 18 12 47 42 177 6.40
Kedah 1 5 4 9 1 4 10 29 63 2.28
Kelantan 111 148 152 99 20 34 135 113 812 29.35
Melaka 0 1 1 0 0 0 2 4 8 0.29
Negeri Sembilan 3 18 25 12 2 3 30 30 123 4.45
Pahang 52 104 136 106 39 33 77 119 666 24.07
Perak 49 55 64 59 19 27 76 175 524 18.94
Perlis 0 0 0 0 0 0 0 1 1 0.04
Pulau Pinang 3 4 2 0 0 0 1 1 11 0.40
Selangor 30 38 40 19 8 14 24 45 218 7.88
Terengganu 20 27 28 13 6 8 21 38 161 5.82
Federal Territory of
Kuala Lumpur 1 0 0 0 0 1 0 1 3 0.11
Federal Territory of
Putrajaya 0 0 0 0 0 0 0 0 0 0.00
The reported cases increased from 2011 to 2014, followed by a transient drop in 2015 and 2016
prior to gradual increase (Figure 3). In 2011, 277 cases were reported, and the number of cases recorded
in 2018 was 598. A similar trend could be observed in the IR of five states: Kelantan, Pahang, Perak,
Terengganu, and Negeri Sembilan. However, the IR in Kelantan and Negeri Sembilan dropped in 2018
as compared to 2017 (Figure 4). Federal Territory of Kuala Lumpur, Pulau Pinang, Melaka, and Perlis
had an IR lower than 1.5 annually. No knowlesi malaria case was reported in the Federal Territory of
Putrajaya throughout the study duration.
Int. J. Environ. Res. Public Health 2020,17, 9271 10 of 21
Int. J. Environ. Res. Public Health 2020, 17, x 10 of 21
Figure 3. P. knowlesi monoinfection cases in Peninsular Malaysia from 2011 to 2018.
Figure 4. State-wide P. knowlesi incidence rate (IR) from 2011 to 2018.
3.3. District-Level Spatial Analysis of P. knowlesi Infection
The district-level P. knowlesi IR from 2011 to 2018 is illustrated in Figure 5. Overall, the spatial
pattern of P. knowlesi transmission was higher in the central-northern region of Peninsular Malaysia.
Gua Musang and Lipis districts consistently recorded higher IR annually throughout the study
period. In 2015, there was a transient drop in the incidence of knowlesi malaria in almost all districts
compared with that in previous years. Only two districts reported a high incidence in the same year.
P. knowlesi incidence peaked in 2018 in 17 districts.
Figure 3. P. knowlesi monoinfection cases in Peninsular Malaysia from 2011 to 2018.
Int. J. Environ. Res. Public Health 2020, 17, x 10 of 21
Figure 3. P. knowlesi monoinfection cases in Peninsular Malaysia from 2011 to 2018.
Figure 4. State-wide P. knowlesi incidence rate (IR) from 2011 to 2018.
3.3. District-Level Spatial Analysis of P. knowlesi Infection
The district-level P. knowlesi IR from 2011 to 2018 is illustrated in Figure 5. Overall, the spatial
pattern of P. knowlesi transmission was higher in the central-northern region of Peninsular Malaysia.
Gua Musang and Lipis districts consistently recorded higher IR annually throughout the study
period. In 2015, there was a transient drop in the incidence of knowlesi malaria in almost all districts
compared with that in previous years. Only two districts reported a high incidence in the same year.
P. knowlesi incidence peaked in 2018 in 17 districts.
Figure 4. State-wide P. knowlesi incidence rate (IR) from 2011 to 2018.
3.3. District-Level Spatial Analysis of P. knowlesi Infection
The district-level P. knowlesi IR from 2011 to 2018 is illustrated in Figure 5. Overall, the spatial
pattern of P. knowlesi transmission was higher in the central-northern region of Peninsular Malaysia.
Gua Musang and Lipis districts consistently recorded higher IR annually throughout the study period.
In 2015, there was a transient drop in the incidence of knowlesi malaria in almost all districts compared
with that in previous years. Only two districts reported a high incidence in the same year. P. knowlesi
incidence peaked in 2018 in 17 districts.
Int. J. Environ. Res. Public Health 2020,17, 9271 11 of 21
Int. J. Environ. Res. Public Health 2020, 17, x 11 of 21
Figure 5. Spatial-temporal distribution of knowlesi malaria in Peninsular Malaysia (2011–2018).
The spatial autocorrelation of knowlesi malaria was evaluated using the 2011–2018 summarized
cases. Global Moran’s I test revealed a significant and positive spatial autocorrelation in the study
area (Global Moran’s I = 0.489, p < 0.001, Z = 7.02). This indicated that there was spatial dependence
and clustering of P. knowlesi incidence in Peninsular Malaysia. Local Moran’s I test identified major
hotspots (high–high spatial clusters) involving eight districts (Table 5 and Figure 6). Two cold spots
(low–low spatial clusters) comprising three districts were identified near major cities. All hotspots
were in regions with low population density (average of 34 people per square km), whereas cold
spots comprised locations with high population density (average of 1305 people per square km).
Low–high spatial clusters were identified in two districts. These two districts (Kinta and Cameron
Highlands) had a low incidence of knowlesi malaria but were surrounded by districts with a high
incidence of the disease.
Table 5. Statistically significant district-level spatial clusters of knowlesi malaria based on IR, 2011–
2018.
Cluster Type District Number of Cases IR LISA Index p-Value
High-high Jeli 95 46.24 5.88 <0.001
Kuala Krai 167 37.35 2.92 0.001
Jerantut 101 32.28 2.16 <0.001
Lipis 329 58.70 5.97 <0.001
Raub 66 25.49 1.51 0.003
Hulu Perak 98 32.23 2.14 0.003
Hulu Terengganu 68 29.42 1.36 0.008
Gua Musang 506 71.68 6.88 <0.001
Low-low Kota Bharu 1 1.36 0.48 0.006
Klang 1 1.03 0.54 0.008
Petaling 3 1.22 0.44 0.008
Low-high Cameron Highlands 2 6.99 −0.58 <0.001
Kinta 48 7.78 −0.25 0.006
Figure 5. Spatial-temporal distribution of knowlesi malaria in Peninsular Malaysia (2011–2018).
The spatial autocorrelation of knowlesi malaria was evaluated using the 2011–2018 summarized
cases. Global Moran’s I test revealed a significant and positive spatial autocorrelation in the study
area (Global Moran’s I =0.489, p<0.001, Z =7.02). This indicated that there was spatial dependence
and clustering of P. knowlesi incidence in Peninsular Malaysia. Local Moran’s I test identified major
hotspots (high–high spatial clusters) involving eight districts (Table 5and Figure 6). Two cold spots
(low–low spatial clusters) comprising three districts were identified near major cities. All hotspots
were in regions with low population density (average of 34 people per square km), whereas cold spots
comprised locations with high population density (average of 1305 people per square km). Low–high
spatial clusters were identified in two districts. These two districts (Kinta and Cameron Highlands)
had a low incidence of knowlesi malaria but were surrounded by districts with a high incidence of
the disease.
Table 5.
Statistically significant district-level spatial clusters of knowlesi malaria based on IR, 2011–2018.
Cluster Type District Number of Cases IR LISA Index p-Value
High-high Jeli 95 46.24 5.88 <0.001
Kuala Krai 167 37.35 2.92 0.001
Jerantut 101 32.28 2.16 <0.001
Lipis 329 58.70 5.97 <0.001
Raub 66 25.49 1.51 0.003
Hulu Perak 98 32.23 2.14 0.003
Hulu Terengganu 68 29.42 1.36 0.008
Gua Musang 506 71.68 6.88 <0.001
Low-low Kota Bharu 1 1.36 0.48 0.006
Klang 1 1.03 0.54 0.008
Petaling 3 1.22 0.44 0.008
Low-high Cameron Highlands 2 6.99 −0.58 <0.001
Kinta 48 7.78 −0.25 0.006
Int. J. Environ. Res. Public Health 2020,17, 9271 12 of 21
Int. J. Environ. Res. Public Health 2020, 17, x 12 of 21
Figure 6. District-level hotspot and cold spot spatial clusters of knowlesi malaria (2011–2018).
3.4. Time Series Analysis of P. knowlesi Incidence in the Hotspot Region
Time series decomposition of the monthly IR of knowlesi malaria in hotspot region involving
eight districts (Jeli, Kuala Krai, Jerantut, Lipis, Raub, Hulu Perak, Hulu Terengganu, and Gua
Musang) is illustrated in Figure 7. The IR of knowlesi malaria in hotspots experienced a transient
downward trend beginning from early 2014 prior to increase in 2017 (Figure 7A). This trend was
similar to the general trend in Peninsular Malaysia. The seasonality of P. knowlesi incidence indicated
the major peak between January and March (Figure 7B).
Figure 6. District-level hotspot and cold spot spatial clusters of knowlesi malaria (2011–2018).
3.4. Time Series Analysis of P. knowlesi Incidence in the Hotspot Region
Time series decomposition of the monthly IR of knowlesi malaria in hotspot region involving
eight districts (Jeli, Kuala Krai, Jerantut, Lipis, Raub, Hulu Perak, Hulu Terengganu, and Gua Musang)
is illustrated in Figure 7. The IR of knowlesi malaria in hotspots experienced a transient downward
trend beginning from early 2014 prior to increase in 2017 (Figure 7A). This trend was similar to the
general trend in Peninsular Malaysia. The seasonality of P. knowlesi incidence indicated the major peak
between January and March (Figure 7B).
Int. J. Environ. Res. Public Health 2020,17, 9271 13 of 21
Int. J. Environ. Res. Public Health 2020, 17, x 13 of 21
Figure 7. (A) Trend of monthly IR of knowlesi malaria in hotspots from 2012 to 2018. (B) Seasonality
of knowlesi malaria incidence in hotspots.
Five candidate SARIMA models with random pattern of the residuals were selected to compare
the model performance. Overall, SARIMA (0,1,1)(2,1,0)12 was considered the best-fitted models of
knowlesi malaria incidence in the hotspot region (AIC = 184.62, BIC = 192.93, RMSE = 0.90, MAPE =
18.51) (Table 6). This forecasting results demonstrated that the model captured the temporal patterns
in 2018 but missed the peak in August (Figure 8).
Table 6. Comparison of candidate SARIMA models based on penalized criteria and accuracy
measurement.
Models AIC BIC RMSE MAPE Ljung–Box Test
#
SARIMA (0,1,0)(2,1,0)12 190.37 196.60 0.93 18.92 0.08
SARIMA (0,1,1)(2,1,0)12 184.62 192.93 0.90 18.51 0.50
SARIMA (0,1,1)(2,1,1)12 186.65 197.03 0.87 18.93 0.19
SARIMA (1,1,0)(2,1,0)12 186.14 194.45 0.89 18.52 0.36
SARIMA (2,1,0)(2,1,0)12 185.91 196.30 0.89 18.17 0.38
AIC: Akaike Information Criterion, BIC: Bayesian Information Criterion, RMSE: root mean square
error, MAPE: mean absolute percentage error, df = degree of freedom; #: p-values of Ljung–Box test.
Figure 8. Time series plot of observed, fitted and forecasted values of knowlesi malaria IR using
SARIMA (0,1,1)(2,1,0)12.
4. Discussion
This study demonstrated the fundamental epidemiology of P. knowlesi infection in Western
Malaysia. P. knowlesi infections were distributed across all age groups with a higher prevalence
among adults aged 20–39 years, specifically in men, who are likely to be more active outdoors and
have greater forest exposure owing to job requirements [27]. A study in Borneo showed that men
accounted for 85% of PCR-confirmed knowlesi malaria cases [14]. This proportion of male P. knowlesi
Figure 7.
(
A
) Trend of monthly IR of knowlesi malaria in hotspots from 2012 to 2018. (
B
) Seasonality of
knowlesi malaria incidence in hotspots.
Five candidate SARIMA models with random pattern of the residuals were selected to compare the
model performance. Overall, SARIMA (0,1,1)(2,1,0)
12
was considered the best-fitted models of knowlesi
malaria incidence in the hotspot region (AIC =184.62, BIC =192.93, RMSE =0.90, MAPE =18.51)
(Table 6). This forecasting results demonstrated that the model captured the temporal patterns in 2018
but missed the peak in August (Figure 8).
Table 6.
Comparison of candidate SARIMA models based on penalized criteria and
accuracy measurement.
Models AIC BIC RMSE MAPE Ljung–Box Test #
SARIMA (0,1,0)(2,1,0)12 190.37 196.60 0.93 18.92 0.08
SARIMA (0,1,1)(2,1,0)12 184.62 192.93 0.90 18.51 0.50
SARIMA (0,1,1)(2,1,1)12 186.65 197.03 0.87 18.93 0.19
SARIMA (1,1,0)(2,1,0)12 186.14 194.45 0.89 18.52 0.36
SARIMA (2,1,0)(2,1,0)12 185.91 196.30 0.89 18.17 0.38
AIC: Akaike Information Criterion, BIC: Bayesian Information Criterion, RMSE: root mean square error, MAPE:
mean absolute percentage error, df =degree of freedom; #: p-values of Ljung–Box test.
Int. J. Environ. Res. Public Health 2020, 17, x 13 of 21
Figure 7. (A) Trend of monthly IR of knowlesi malaria in hotspots from 2012 to 2018. (B) Seasonality
of knowlesi malaria incidence in hotspots.
Five candidate SARIMA models with random pattern of the residuals were selected to compare
the model performance. Overall, SARIMA (0,1,1)(2,1,0)12 was considered the best-fitted models of
knowlesi malaria incidence in the hotspot region (AIC = 184.62, BIC = 192.93, RMSE = 0.90, MAPE =
18.51) (Table 6). This forecasting results demonstrated that the model captured the temporal patterns
in 2018 but missed the peak in August (Figure 8).
Table 6. Comparison of candidate SARIMA models based on penalized criteria and accuracy
measurement.
Models AIC BIC RMSE MAPE Ljung–Box Test
#
SARIMA (0,1,0)(2,1,0)12 190.37 196.60 0.93 18.92 0.08
SARIMA (0,1,1)(2,1,0)12 184.62 192.93 0.90 18.51 0.50
SARIMA (0,1,1)(2,1,1)12 186.65 197.03 0.87 18.93 0.19
SARIMA (1,1,0)(2,1,0)12 186.14 194.45 0.89 18.52 0.36
SARIMA (2,1,0)(2,1,0)12 185.91 196.30 0.89 18.17 0.38
AIC: Akaike Information Criterion, BIC: Bayesian Information Criterion, RMSE: root mean square
error, MAPE: mean absolute percentage error, df = degree of freedom; #: p-values of Ljung–Box test.
Figure 8. Time series plot of observed, fitted and forecasted values of knowlesi malaria IR using
SARIMA (0,1,1)(2,1,0)12.
4. Discussion
This study demonstrated the fundamental epidemiology of P. knowlesi infection in Western
Malaysia. P. knowlesi infections were distributed across all age groups with a higher prevalence
among adults aged 20–39 years, specifically in men, who are likely to be more active outdoors and
have greater forest exposure owing to job requirements [27]. A study in Borneo showed that men
accounted for 85% of PCR-confirmed knowlesi malaria cases [14]. This proportion of male P. knowlesi
Figure 8.
Time series plot of observed, fitted and forecasted values of knowlesi malaria IR using
SARIMA (0,1,1)(2,1,0)12.
4. Discussion
This study demonstrated the fundamental epidemiology of P. knowlesi infection in Western
Malaysia. P. knowlesi infections were distributed across all age groups with a higher prevalence among
adults aged 20–39 years, specifically in men, who are likely to be more active outdoors and have greater
forest exposure owing to job requirements [
27
]. A study in Borneo showed that men accounted for 85%
Int. J. Environ. Res. Public Health 2020,17, 9271 14 of 21
of PCR-confirmed knowlesi malaria cases [
14
]. This proportion of male P. knowlesi patients is similar to
that reported in nine states in Peninsular Malaysia (79.92%–93.33%). A lower incidence of P. knowlesi
cases was observed among children and the elderly, which could be associated with limited outdoor
activities and lower risk of getting bit by infected Anopheles mosquitoes.
Individuals infected with P. knowlesi were involved in various types of occupation. However,
most (53.96%) had occupations related to agriculture and the forest, and this phenomenon has been
shown in a previous study [
15
]. Nonetheless, types of occupation alone may not provide a true
reflection of the risk of P. knowlesi infection. Recreational forest activities such as bird watching,
hiking, and camping may also expose an individual to infective mosquito bites. Rural and suburban
settlements located close to the forest and in forested areas mostly comprise the Malay community,
which suggests a greater burden of P. knowlesi infection among Malays than among other ethnic groups.
In this study, Kelantan, Pahang, and Perak had consistently recorded high burden of knowlesi
malaria. These states have the largest settlement of aborigine communities [
28
]. Aborigines made
up one-tenth of the total indigenous knowlesi malaria cases. Prior to our study, human malaria
parasitic infections had been frequently reported among the aborigine population in Peninsular
Malaysia [29–31]
. Moreover, researchers detected the presence of submicroscopic P. knowlesi infections
among asymptomatic individuals within these communities [
32
]. Aborigine communities are
considered at a high-risk of exposure to malaria. This is because their settlements are located
in the forests and forest fringes, and many are still dependent on forest resources for subsistence [
33
,
34
].
Nevertheless, logistical hurdles and the presence of submicroscopic incidence within these communities
have challenged the current microscopic and PCR-based diagnostic approaches. These justify the
necessity for development of portable and highly sensitive P. knowlesi specific rapid diagnostic tests.
Spatial analysis indicated a high IR of P. knowlesi concentrated in the central-northern region
of Peninsular Malaysia. Within this region, Gua Musang and Lipis districts reported the highest
IR when compared with other districts. They are neighboring districts and most infected patients
worked in the agricultural sector, implying frequent exposure to the forest, forest-edge, and plantation
setting. Thus, this increased probability of contact with Anopheles mosquitoes as well as macaque
populations. Prior to 2015, many agricultural, logging, and quarrying activities occurred in the Gua
Musang district [
35
]. These activities included exploitation of secondary forests and permanent forest
reserves, which potentially led to the spill over of the macaque population to human settlements.
A 2011 census on long-tailed macaque density covering all Peninsular Malaysia states estimated that the
macaque population was the highest in agricultural areas, followed by oil palm plantations and urban
areas, possibly due to abundant food resources for macaques [
36
]. The mosquito vectors may have
followed their macaque hosts and adapted to these habitats [
37
]. To date, point prevalence of P. knowlesi
has been established for wild macaque population in Johor (3%), Perak (4%), Pahang (26%) [
38
],
and Selangor (30%) [
39
]. Additionally, incriminated vectors for P. knowlesi including Anopheles cracens
and Anopheles introlatus were found in Pahang and Selangor, respectively, with a P. knowlesi positive
rate of less than 2% [
9
,
40
]. Natural simian malaria infections in humans tend to occur when humans
break the normal mosquito–macaque circulation chain in the forested area [
41
]. Current knowledge
on P. knowlesi prevalence among macaques and Anopheles mosquitoes is scarce, and there is a need to
increase surveillance coverage for providing improved information on parasite prevalence in whole
Peninsular Malaysia.
A decline in P. knowlesi incidence was observed in 2015 and 2016, whereby the number of knowlesi
malaria cases reported in Peninsular Malaysia dropped below 150 annually. Possible explanations
for this change include a transient reduction in case detection activities, reduction in vector density,
reduction in macaque population, or reduction in human-mosquito contacts. Malaria elimination efforts
were intensified to reduce the population of vectors responsible for human malaria such as P. malariae,
P. vivax, and P. falciparum, and these activities could have temporarily reduced the P. knowlesi vector
population as well. These measures include the distribution of ITNs, larviciding, residual spraying,
and repellents to high-risk groups to reduce the malaria vector population and human-mosquito
Int. J. Environ. Res. Public Health 2020,17, 9271 15 of 21
contacts. A successful malaria health policy could lead to a reduction in malaria cases [
42
]. In addition,
the climate change phenomenon, which led to an anomalous rainfall pattern and strong drought in
Southeast Asia in 2015, accompanied by severe haze episodes, could have contributed to changes in
vector density and reduced malaria transmission [
14
,
43
,
44
]. This highlights the need to assess the
effect of extreme climatic variability on changes in P. knowlesi transmission [42].
A significant rebound in P. knowlesi transmission was observed after 2016. This may be due to
drastic changes in the transmission dynamics of the parasite between humans, macaques, and vectors
caused by agricultural expansion and forest exploration. According to the Forestry Department
of Peninsular Malaysia statistical report, approximately one million hectares of total forested areas
have been lost from 2016 to 2018, and the permanent reserved forest area has decreased from
4.92 million hectares in 2016 to 4.80 million hectares in 2018 [
18
]. Peninsular Malaysia has experienced
large-scale deforestation due to intensified agricultural activities such as harvesting oil palm and
rubber, timber production, and rapid urban expansion since the 1970s [
45
]. Establishment of crop
plantations increases the vectors’ natural breeding site and human exposure to these vectors’ breeding
sites [
46
]. In contrast to P. falciparum or P. vivax transmission, people infected by P. knowlesi are usually
exposed to agricultural settings or forests instead of contracting the infection at home or surrounding
areas owing to the exophagic nature of the vectors [
37
]. In Sabah, a higher P. knowlesi transmission
was observed in large intact forest patches within 5 km of households, pulpwood plantations within
3 km of households, and oil palm plantations with fragmented landscapes [
15
]. Malaria elimination
strategies such as ITN and residual spraying applied within the vicinity of houses have proven to
be effective in interrupting human malaria transmission but less protective against simian malaria
vectors that feed on its host predominantly in the forest. However, personal insecticide usage has been
proven to reduce the risk of exposure to P. knowlesi [
15
]. Although efforts have been made to distribute
repellents to agricultural workers, their active participation in using these repellents routinely at work
is required. Furthermore, loss of cross-species immunity may have contributed to the surge of knowlesi
malaria incidence especially after 2016 following a decrease in human malaria parasites incidence,
and this was similarly postulated in a previous study [
14
]. For instance, early studies presented that
individuals with history of P. vivax infection were more resistant to P. knowlesi infection than those
without P. vivax infection history, and recently, P. knowlesi erythrocyte invasion inhibition by P. vivax
antibodies was reported [47–49].
As both Peninsular Malaysia and Malaysian Borneo share similar geographical and cultural
characteristics to a certain extent, there are other comparative points from previous studies that can
reflect the transmission of P. knowlesi infections in Peninsular Malaysia. Both Peninsular Malaysia and
Malaysian Borneo experienced an increase in P. knowlesi infections despite a dramatic decline in other
human malaria parasite infections [
16
]. In Sabah, a higher rainfall was associated with an increase
in knowlesi malaria cases after three months [
14
,
50
]. An increase in rainfall causes the formation of
water pockets, which are ideal for mosquito breeding. Nevertheless, a combination of environmental
factors, including temperature, rainfall, humidity, and land use, could serve as predictors of disease
transmission. In addition, community-level surveillance using serological markers demonstrated that
P. knowlesi exposure was positively associated with age, the male sex, forest activity, and contact with
macaques but negatively with personal insecticide practice and residing at a higher altitude [
15
,
51
].
Similar investigations should be conducted to understand the environmental and exposure risks based
on these factors, particularly in Peninsular Malaysia.
In this study, some districts in Perak, Kelantan, Pahang, and Terengganu states were found to
have P. knowlesi hotspots. These findings can guide malaria control programs in strategizing effective
malaria intervention, specifically in these districts. Moreover, the monitoring of districts clustered as
hotspots is crucial because disease transmission may spill over to their neighbors owing to cross-district
movement of macaque populations with potential carriage of malaria parasites or long-distance spread
of infective vectors. Studies in Africa suggested that some species of malaria vectors can fly over
hundreds of kilometers [
52
,
53
]. To date, the dispersal behaviors of malaria vector species in Malaysia
Int. J. Environ. Res. Public Health 2020,17, 9271 16 of 21
are not fully understood, and this has certainly revealed a knowledge gap for further studies to
determine the connectivity between bionomics of vectors and malaria incidence.
SARIMA models were widely applied by researchers to predict burden of infectious diseases,
including malaria [
54
–
56
], dengue [
57
], influenza [
58
,
59
], and mumps [
60
]. SARIMA (0,1,1)(2,1,0)
12
were found to be the best suited models for future short time frame prediction of knowlesi malaria
incidence in major malaria hotspots in Peninsular Malaysia. Although all the observed IR from
January 2018 to December 2018 can be predicted within 80% prediction interval, the model missed
the peak in August, which indicated that some latent parameters should be included in the future to
improve prediction accuracy. These parameters could be associated with vectors or reservoir hosts
abundance, environmental factors, and public health interventions. Comparatively, researchers in
Sarawak developed a non-seasonal autoregressive model to predict statewide increasing trend of
P. knowlesi transmission up to 2040; however, such long-term forecasting might encounter various
uncertainty issues [
61
]. The short-term forecasting model developed in this study might be more
feasible for control strategy modification.
This is the first study that utilized spatial data analysis to generate the spatial distribution of
knowlesi malaria at the district level covering the entire Peninsular Malaysia. Nonetheless, this study
has some limitations. First, the study was descriptive in nature, with no control group for risk factor
analysis. Second, we did not include environmental parameters in the analysis, which are important to
better describe the transmission dynamics of P. knowlesi in the study area. Third, data related to the
macaque reservoir host were not included in this study because there was a lack of comprehensive
surveillance data for macaques infected with P. knowlesi from 2011 to 2018. Further studies could be
conducted by considering the impact of environmental variations on the transmission of knowlesi
malaria. Factors such as rainfall, relative humidity, temperature, water bodies, and normalized
difference vegetation index can be used to spatially assess the malaria risk factors. The availability
of macaque host data could assist in understanding the role of the macaque population, particularly
those carriers for P. knowlesi, in disease transmission to humans. Although this study does not involve
macaque data, its output could suggest focal areas for surveillance of P. knowlesi within the macaque
population in future research. While Malaysia is moving forward to end the transmission of human
malaria, the emergence of P. knowlesi could become the next challenge for malaria elimination programs.
Advancement in our knowledge about the ecology of P. knowlesi could help policy makers develop
effective P. knowlesi-specific control strategies in Peninsular Malaysia.
5. Conclusions
This study highlighted the spatial distribution of P. knowlesi at the district level from 2011 to 2018
in Peninsular Malaysia. Coupled with the temporal forecasting method using SARIMA, the findings
from this study could assist malaria elimination programs in targeting P. knowlesi hotspots in Peninsular
Malaysia for programmatic intervention.
Author Contributions:
W.K.P., T.-W.C., Y.L.L., and M.Y.F. conceptualized and designed the study. M.H.A.H., J.J.,
and R.N.M. were involved in data collection and provided the dataset for analysis. W.K.P. and T.-W.C. conducted
the data analysis. W.K.P. wrote the manuscript. All authors have read and agreed to the published version of
the manuscript.
Funding:
This study was supported by the Ministry of Education Malaysia (grant number LR002A-2018) and the
Ministry of Science and Technology, Taiwan (MOST108-2638-H-002-002-MY2).
Acknowledgments:
The authors thank the Ministry of Health Malaysia for providing data in support of this study.
Conflicts of Interest: The authors declare no conflict of interest.
Int. J. Environ. Res. Public Health 2020,17, 9271 17 of 21
Appendix A
Int. J. Environ. Res. Public Health 2020, 17, x 17 of 21
Appendix
Figure A1. Number of indigenous malaria cases according to year and Plasmodium species.
Figure A2. Types of occupation of indigenous knowlesi malaria patients. The proportion of
occupations with exposure to forest is compared with other types of occupation.
Figure A1. Number of indigenous malaria cases according to year and Plasmodium species.
Int. J. Environ. Res. Public Health 2020, 17, x 17 of 21
Appendix
Figure A1. Number of indigenous malaria cases according to year and Plasmodium species.
Figure A2. Types of occupation of indigenous knowlesi malaria patients. The proportion of
occupations with exposure to forest is compared with other types of occupation.
Figure A2.
Types of occupation of indigenous knowlesi malaria patients. The proportion of occupations
with exposure to forest is compared with other types of occupation.
Int. J. Environ. Res. Public Health 2020,17, 9271 18 of 21
Int. J. Environ. Res. Public Health 2020, 17, x 18 of 21
Figure A3. (A) Autocorrelation function (ACF) and partial autocorrelation function (PACF) plots of
observed IR of knowlesi malaria from 2012 to 2017. (B) ACF and PACF plots of one-order non-
seasonal and seasonal differenced knowlesi malaria IR series (d = 1 and D = 1). (C) ACF and PACF
plots of fitted SARIMA (0,1,1)(2,1,0)12 model residuals. The dashed lines represent 95% confidence
intervals.
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8. Singh, B.; Lee, K.S.; Matusop, A.; Radhakrishnan, A.; Shamsul, S.S.; Cox-Singh, J.; Thomas, A.; Conway,
D.J. A large focus of naturally acquired Plasmodium knowlesi infections in human beings. Lancet 2004, 363,
1017–1024, doi:10.1016/S0140-6736(04)15836-4.
9. Vythilingam, I.; Lim, Y.A.; Venugopalan, B.; Ngui, R.; Leong, C.S.; Wong, M.L.; Khaw, L.; Goh, X.; Yap, N.;
Sulaiman, W.Y.; et al. Plasmodium knowlesi malaria an emerging public health problem in Hulu Selangor,
Selangor, Malaysia (2009–2013): Epidemiologic and entomologic analysis. Parasit. Vectors 2014, 7, 436,
doi:10.1186/1756-3305-7-436.
Figure A3.
(
A
) Autocorrelation function (ACF) and partial autocorrelation function (PACF) plots of
observed IR of knowlesi malaria from 2012 to 2017. (
B
) ACF and PACF plots of one-order non-seasonal
and seasonal differenced knowlesi malaria IR series (d =1 and D =1). (
C
) ACF and PACF plots of
fitted SARIMA (0,1,1)(2,1,0)12 model residuals. The dashed lines represent 95% confidence intervals.
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