1
1 Spatial and epidemiologic features of dengue in Sabah,
2 Malaysia
3
4 Amanda Murphy1,2*, Giri Shan Rajahram3,4, Jenarun Jilip5, Marilyn Maluda5, Timothy
5 William4,6, Wenbiao Hu7, Simon Reid8, Gregor J. Devine1^, Francesca D. Frentiu2^.
6
7
8
9 1 Mosquito Control Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, 10 Australia
11 2 School of Biomedical Sciences, and Institute for Health and Biomedical Innovation, 12 Queensland University of Technology, Brisbane, Australia
13 3 Queen Elizabeth Hospital, Ministry of Health Malaysia, Kota Kinabalu, Malaysia
14 4 Infectious Disease Society of Kota Kinabalu-Menzies School of Health Research Clinical 15 Research Unit, Kota Kinabalu, Malaysia
16 5 Sabah Department of Health, Ministry of Health Malaysia, Kota Kinabalu, Malaysia
17 6 Gleneagles Kota Kinabalu Hospital Sabah, Kota Kinabalu, Malaysia
18 7 School of Public Health and Social Work, Queensland University of Technology, Brisbane, 19 Australia
20 8 School of Public Health, University of Queensland, Brisbane, Australia
21
22
23 * Corresponding author
24 E-mail: [email protected]
25
26
27 ^ These authors contributed equally to this work
28
29
30 Keywords: dengue, rural, Sabah, Aedes albopictus, Borneo, South East Asia
31
32
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33 Abstract3435 In South East Asia, dengue epidemics have increased in size and geographical distribution in
36 recent years. Most studies investigating dengue transmission and control have had an urban
37 focus, while less consideration is currently given to rural settings, or where urban and rural
38 areas overlap. We examined the spatiotemporal distribution and epidemiological
39 characteristics of reported dengue cases in the predominantly rural state of Sabah, in
40 Malaysian Borneo – an area where sylvatic and urban circulation of pathogens are known to
41 intersect. We found that annual dengue incidence rates were spatially variable over the 7-
42 year study period from 2010-2016 (state-wide mean annual incidence of 21 cases/100,000
43 people; range 5-42/100,000), but were highest in rural localities in the western districts of
44 the state (Kuala Penyu, Nabawan, Tenom and Kota Marudu). The eastern districts exhibited
45 lower overall dengue rates; however, we noted a concentration of severe (haemorrhagic)
46 dengue cases (44%) in Sandakan and Tawau districts. Dengue incidence was slightly higher
47 for males than females, and was significantly higher for both genders aged between 10 and
48 29 years (24/100,000; p=0.029). The largest ever recorded outbreaks occurred during 2015-
49 2016, with the vector Aedes albopictus found to be most prevalent in both urban and rural
50 households (House Index of 64%), compared with Ae. Aegypti (15%). These findings suggest
51 that dengue outbreaks in Sabah are driven by the sporadic expansion of dengue virus in both
52 urban and rural settings. This may require tailoring of preventative strategies to suit
53 different transmission ecologies across Sabah. Further studies to better understand the
54 drivers of dengue in Sabah may aid dengue control efforts in Malaysia, and more broadly in
55 South East Asia.
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56 Author summary5758 In order to combat the rising regional incidence of dengue in South East Asia, the drivers of
59 transmission must be better characterised across different environmental settings. We
60 conducted the first retrospective analysis of dengue epidemiology in the predominantly rural
61 state of Sabah, Malaysia, where both urban and sylvatic transmission cycles exist. Human
62 notification data over a 7-year period were reviewed and spatiotemporal and demographic
63 risk factors identified. We found:
64 1. Urban habitats and population density are not the only determinants mediating the
65 spread of epidemic dengue in Sabah. Case from both urban and rural localities
66 contributed equally to dengue outbreaks.
67 2. Human demographic risk factors included being aged between 10 and 29 years, and
68 being male.
69 3. High incidence areas for dengue do not predict the occurrence of severe dengue. Severe
70 dengue was largely localised to lower incidence districts in the east of the state.
71 4. The sole presence of Aedes albopictus in and around the majority of urban and rural
72 case households suggests that this vector may play a major role in facilitating outbreaks.
73 A complex interplay of risk factors likely mediates dengue transmission in Sabah, influenced
74 by both regional climate trends and localised human and ecological influences. This study
75 emphasises that the increasing spread of dengue in urban South East Asia is also mirrored in
76 more rural areas, and suggests a need for control strategies that address both urban and
77 rural dengue risk.
78
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79 Introduction8081 Dengue is the most rapidly spreading vector-borne disease in the world, and the most
82 prevalent arboviral disease of humans (1). Now endemic in more than 100 countries, the
83 disease causes an enormous burden on communities and health care systems in tropical and
84 sub-tropical regions (2). The causative agent of dengue is dengue virus (DENV), transmitted
85 between humans by Aedes mosquitoes across a range of domestic and sylvatic
86 environments. Urban expansion, human migration, travel and trade have facilitated an
87 increasing number of infections, primarily in Asia, Africa and the Americas (3, 4). These areas
88 experience up to 70% of the estimated 390 million annual dengue infections worldwide (1,
89 4). Explosive outbreaks have become common in recent decades, and both classical and
90 severe (haemorrhagic) forms of dengue now occur in previously unaffected countries (1, 3,
91 5). South East Asia has one of the highest burdens of dengue, following marked increases in
92 the number, severity and geographical distribution of dengue epidemics since the 1950s.
93 During this dramatic expansion, the four virus serotypes (DENV 1-4) have become well-
94 established and commonly co-circulate within the region (6).
95 In Malaysia, dengue has been considered a major public health problem since 1973 (7), with
96 regular epidemics resulting in significant morbidity and economic burden (8, 9). The majority
97 of reported cases are concentrated in the large, urban cities of Kuala Lumpur and Penang,
98 which are located on the Malaysian peninsula. The circulation of all DENV serotypes has
99 been documented across the country, as well as the presence of unique sylvatic strains (10-
100 12). As with many South East Asian countries, the characterisation and control of
101 transmission in Malaysia is primarily focused on highly populated urban areas (13). The
102 majority of spatial and eco-epidemiological studies to date have therefore focused on
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103 peninsular Malaysia, and relatively few studies have explored the factors driving
104 transmission in rural parts of the country.
105 The Malaysian states of Sabah and Sarawak, located on the island of Borneo, report lower
106 incidence rates than mainland Malaysia (14) and patterns of transmission in these states are
107 not well characterised. The island possesses rapidly developing urban areas in close
108 proximity to disturbed forest environments, with potential risk of spill-over of sylvatic
109 pathogens to human populations (15). Sabah state, positioned on the northern tip of
110 Borneo, reports the highest incidence of the sylvatic malaria parasite Plasmodium knowlesi,
111 with transmission risk linked to deforestation (16, 17). The emergence of other zoonotic
112 pathogens has also been documented in Sabah (18, 19), including Zika virus in 2015 (20).
113 Given the marked environmental change occurring in Sabah, and the increase in dengue
114 cases noted in recent years (12, 14), it is essential from a public health perspective to
115 understand current transmission patterns and their drivers. This study examined the
116 epidemiology of dengue in the state of Sabah, in Malaysian Borneo, between 2010 and 2016.
117 We aimed to document recent spatial and temporal trends of dengue disease, and to
118 identify some potential risk factors driving DENV transmission and spread in this
119 understudied region of the country.
120
121 Methods122
123 Ethics statement
124 This study was approved by the Medical Research and Ethics Committee (MREC), Ministry of
125 Health Malaysia; and the Human Research Ethics Committee (HREC) of the QIMR Berghofer
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126 Medical Research Institute, Brisbane, Australia. All human case data analysed were
127 anonymized.
128 Study site
129 The Malaysian state of Sabah lies at the most north-eastern tip of the island of Borneo. It
130 borders the Malaysian state of Sarawak and the Indonesian province of Kalimantan (Fig. 1).
131 The climate is tropical, with high humidity and rainfall throughout the year. Sabah has a
132 geographical area of 73,904 km2 and is divided into 25 districts (21). The state’s population
133 density is second lowest in the country (44 people/km2), after Sarawak (20 people/km2), and
134 Sabah also has one of the lowest overall proportions of urban population (54%) in the
135 country (14). Within Sabah, Kota Kinabalu district has the highest population density (1,397
136 people/km2), where the capital city of the same name is located.
137
138 Fig 1. Map of Malaysia and Sabah state.
139 Peninsula Malaysia and Malaysian Borneo are shown, along with the 13 Malaysian states 140 and 2 territories. States are coloured according to their population density, expressed as 141 number of people per square km. The island of Borneo includes the Malaysian states Sabah 142 and Sarawak, and is also shared by the country of Brunei and the Indonesian province of 143 Kalimantan. Inset: Sabah state, showing its 25 districts. The three largest cities in the state 144 are indicated by black circles: the capital city Kota Kinabalu, Sandakan and Tawau.
145
146 Historically, Sabah was almost entirely covered by primary rainforest and still has the
147 second-highest proportion of forested areas in the country (60%) after Sarawak (64%). It also
148 has high rates of forest loss, with monocultures of rubber and palm plantations now
149 estimated to cover 36-56% of the land area (15, 21). Sabah has the second-highest
150 proportion of Indigenous people in the country (25%) after Sarawak (30%), as well as the
151 highest proportion of non-Malaysian residents (25%) of all the Malaysian states (14).
152
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153 Epidemiological data
154 State-wide data from monthly notified cases of dengue between the years 2010 and 2016
155 were obtained from the Sabah State Department of Health (Jabatan Kesihatan Negeri
156 Sabah), Malaysian Ministry of Health. Prior to 2010, detailed data were not available in
157 disaggregated and electronic format. Variables analysed included age, sex, district and
158 locality of each case residence (based on home address), disease severity and outcome
159 (survival or death), and diagnostic tests performed (IgG, IgM and/or NS1). In our dataset,
160 cases from 2011-2016 were designated as residing in either urban or rural localities (the
161 smallest residential geographical unit) by the Sabah Ministry of Health (MoH). MoH
162 designation of locality status is based on the Malaysian Department of Statistics definitions,
163 where urban localities are gazetted census areas with 10,000 people or more, with ≥60% of
164 the working population (≥15 years) engaged in non-agricultural activities (14). Population
165 and demographic data were obtained from the Malaysian Department of Statistics, for the
166 year 2010. Incidence rates were calculated using population projections for each year, based
167 on census data from the year 2010, along with annual growth rate projections as per the
168 published growth rate in Sabah (22).
169 During the study period, clinical cases were identified using World Health Organization
170 (WHO) guidelines using clinical symptoms and/or positive NS1 or serology (presence of IgM
171 or IgG) (23). From 2014 onwards, Malaysian national notification guidelines were modified,
172 in line with WHO advice, to require a positive laboratory diagnostic test (either NS1 and/or
173 IgM/IgG serology) in addition to the presence of clinical symptoms, and case notification
174 within 24 hours of diagnosis (24, 25). Therefore, the majority of cases prior to 2014 were
175 clinically diagnosed (with 30-50% per year confirmed by laboratory tests in our dataset),
176 while cases from 2014-2016 were 100% laboratory confirmed.
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177 Entomological data
178 Entomological surveillance data (number of larvae, mosquito species identified) were
179 generated from active surveillance of potential aquatic habitats, primarily water-holding
180 containers, in and around 719 case residences inspected during the 2015-16 outbreaks. Of
181 these, 255 (36%) of residences were in a locality designated as urban by local public health
182 authorities, 437 (61%) were considered rural, and 27 (4%) had no rural or urban designation
183 recorded. Where mosquito larvae were found in or around a case household, samples were
184 taken to local public health laboratories for species identification. The presence or absence
185 of one or more species per household was recorded, and the House Index (HI) was
186 calculated as the proportion of houses infested with larvae and/or pupae (26). HI was also
187 calculated for each mosquito species present in larvae-positive households.
188 Data analysis
189 We assessed seasonal characteristics of the temporal distribution of cases using a seasonal
190 trend decomposition procedure in SPSS software. The procedure is based on the Census
191 Method I, otherwise known as the ratio-to-moving-average method where time series data
192 are separated into a seasonal component, a combined trend and cycle component, and an
193 "error" or irregular component (27). The seasonal component is then isolated from the
194 overall and irregular trends through a multiplicative model. Seasonal decomposition analysis
195 was applied to monthly dengue case numbers across the 7-year period to examine the
196 seasonal trends of case notifications across Sabah.
197 Annual and cumulative incidence of dengue was calculated using the number of notifications
198 per month and Sabah population estimates based on the 2010 Malaysian census. Incidence
199 rates were standardized for age and sex using census data and plotted for each of the 25
200 administrative districts. Ages of cases were grouped into four categories to broadly separate
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201 young children from older children and adults (0-9, 10-29, 30-49 and ≥50). The statistical
202 significance of observed differences between means was determined using the Kruskal
203 Wallis test. SPSS Statistics software (SPSS, IBM New York USA; version 23) was used for data
204 analyses, with statistical significance set at p<0.05. Spatial maps of Malaysia and Sabah
205 dengue cases and incidence were created using ArcGIS (Esri Redlands USA; version 10.5.1).
206 We assessed overall and annual trends of rural versus urban cases at the state-wide level for
207 a 6-year period where locality status was available (2011-2016). This included a total of
208 9,791 cases. Of these, 756 (7.7%) cases were missing a designated locality status (rural or
209 urban). We classified these cases with no locality status as having ‘unspecified’ localities, and
210 excluded these from rural-urban incidence calculations. For the remaining 9,035 cases, we
211 calculated the total proportions and incidence rates for urban and rural cases, using
212 population projections calculated from state-wide rural-urban population data published in
213 2010 (22). At district level, we calculated annual and overall proportions of rural and urban
214 cases per district. Where cases with unspecified localities were included in analyses (Tables
215 1, 2 and S1), the proportion of unspecified localities were indicated. Annual and overall
216 relative risks (RR) of dengue for each individual district were calculated using:
217 RR =Observed incidence rateExpected incidence rate
218 where the expected incidence rate for each district is based on the mean rate for the state
219 multiplied by the population of each district. A RR value > 1 indicates increased incidence of
220 dengue in that location compared to the expected (mean) incidence, and a value < 1
221 indicates lower than expected dengue incidence.
222
223
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224 Results225
226 Temporal trends across the state
227 A total of 11,882 dengue cases were reported in Sabah during the 7-year study period, with
228 25 deaths. Cases were reported year-round, with outbreaks commonly occurring in the
229 second half of the year between July and December, sometimes continuing into January and
230 February (Fig 2). Seasonal decomposition analysis showed that, on average, notifications
231 peaked each January, with the highest risk period being between November and March.
232 Smaller peak periods were also observed occasionally in July and October (S1 Fig).
233
234 Fig 2. Temporal pattern of dengue in Sabah, 2010-2016.
235 The monthly number of reported dengue cases per year are shown (primary vertical axis), 236 and the corresponding monthly incidence rate (secondary vertical axis). The change in case 237 definition during the study period is indicated by different colour bars: grey bars during the 238 years 2010-2013 where case diagnoses were predominantly clinically-based (with or without 239 laboratory confirmation), and blue bars for the period 2014-2016 where all cases were 240 laboratory confirmed.
241
242 Outbreaks varied in magnitude between years, with the largest outbreaks in 2010 and from
243 2015-2016 (Fig 2). During these large outbreak years, state-wide annual incidence peaked at
244 between 35 and 43 cases per 100,000, respectively. Conversely, incidence rates dropped to
245 5-9 per 100,000 during the smaller outbreak years between 2011 and 2013. The mean state-
246 wide annual incidence rate across the 7 years was 21 cases per 100,000 people.
247 For the period 2011-2016, state-wide mean annual incidence of dengue in urban localities
248 was 44/100,000 versus 47/100,000 for rural localities, and annual rates of dengue in urban
249 and rural localities often contributed similarly to the overall burden (Fig 3). However, there
250 was a notable difference during the large outbreaks of 2015 and 2016, when the highest
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251 incidence localities appeared to switch between being predominantly urban in 2015 to
252 predominantly rural in 2016.
253
254 Fig 3. State-wide annual incidence of dengue in rural and urban localities, 2011-2016.
255 Annual incidence rates across the state are shown for cases residing in either urban or rural 256 localities, over a 6-year period.
257
258 Demographic trends
259 Analyses of demographic trends across Sabah indicated a slightly higher proportion of male
260 dengue cases (60%) than females (40%). After adjusting for differences in population
261 proportions, incidence rates were not significantly different between the two (29/100,000
262 for males and 20/100,000 for females, p=0.32; Fig 4). This was relatively consistent across all
263 Sabah districts; however, there were some districts with above-average proportions of male
264 cases – in particular, in Tongod and Kinabatangan (75% and 65% male cases, respectively).
265
266 Fig 4. Incidence of dengue in Sabah by age group and gender, 2010-2016.
267 Age- and gender-adjusted incidence rates across Sabah during the 7-year period are shown 268 for males, females and for both genders. * Age-group 10-29 for both genders had a 269 statistically significantly higher mean rate, p=0.029.
270
271 Older children and young adults were the dominant age groups affected by dengue (Fig 4),
272 with the majority of cases occurring between 10 and 29 years (mean annual incidence of 24
273 cases/100,000; 47% of the burden across age groups), followed by 30-49 years (mean 13
274 cases/100,000/year; 26% of total cases). The median age of all notifications was 25. After
275 adjusting for differences in population proportions, the 10-29 age group had significantly
276 higher incidence than the other age groups (p=0.029). The lowest proportion of notifications
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277 occurred below 10 years of age (mean 6/100,000/year), followed by those 50 years of age
278 and above (8/100,000/year).
279 Spatial trends across districts
280 District-level incidence rates were highly variable each year, with a mean annual rate of 50
281 cases/100,000 (range 19-161 cases/100,000) across the 7 years (Table 1, S2 Fig). High annual
282 variability meant that there was here was no significant difference in mean incidence rates
283 between the districts overall (p=0.462); however, the highest mean incidence rates were
284 found in districts in the west of the state with relatively low human population density,
285 including Kuala Penyu, Nabawan, Tenom and Kota Marudu. These high incidence districts
286 also displayed the greatest extremes in annual dengue rates, ranging between 15 and 942
287 cases/100,000 each year (S2 Fig). The overall relative risks were highest in Kuala Penyu, Kota
288 Marudu and Kudat districts (RR=3.5, 2.1 and 1.8, respectively; Table 1). The 4 highest-
289 incidence districts reported a low proportion of cases residing in urban localities (0-12%;
290 Table 1). Lower, less variable incidence rates were recorded from some of the central and
291 eastern districts including Kinabatangan, Tongod, Kunak and Tawau (annual incidence range
292 of 3-62 cases/100,000 each year). These districts also had some of the lowest relative risks
293 (RR=0.3, 0.4, 1, and 0.8, respectively; Table 1, S2 Fig), along with a wide range in their
294 proportions of urban cases (4-73%).
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295 Table 1. Summary of population and dengue burden across Sabah, 2010-2016.
DistrictHuman
population(2010)
Population density
(people/km2)
Proportion of cases from
urban localities*
Mean annual incidence
(per 100,000)
Severe dengue mean annual
incidence(per 100,000)
Overall relative risk
Number of dengue deaths
Beaufort 66,406 38 0.06 49 0.2 0.9 0Beluran# 106,632 14 0.14 34 0.9 0.8 1Keningau 177,735 50 0.11 57 0.2 0.9 0Kinabatangan 150,327 23 0.04 19 0.0 0.3 0Kota Belud 93,180 67 0.02 52 0.7 1.4 0Kota Kinabalu 462,963 1,315 0.85 57 0.2 1 5Kota Marudu 68,289 36 0.02 79 0.4 2.1 1Kuala Penyu 19,426 43 0.06 161 0.0 3.5 0Kudat 85,404 66 0.50 52 0.5 1.8 1Kunak 62,851 55 0.62 33 1.3 1 0Lahad Datu 206,861 28 0.42 45 0.8 1.3 2Nabawan^ 32,309 5 0.00 88 0.0 1.6 0Papar 128,434 103 0.20 31 0.0 0.6 0Penampang 125,913 270 0.51 74 0.6 1.3 1Pitas 38,764 27 0.29 36 0.0 0.8 0Putatan** 55,864 1,397 0.32 73 0.0 1 2Ranau 95,800 26 0.09 37 0.1 0.7 1Sandakan 409,056 180 0.80 57 1.0 1.1 4Semporna 137,868 120 0.53 46 1.1 1 2Sipitang 35,764 13 0.14 56 0.0 1.2 0Tambunan 36,297 27 0.00 37 0.0 0.8 0Tawau 412,375 67 0.73 33 0.8 0.8 5Tenom 56,597 13 0.12 84 0.7 1.5 0Tongod** 36,192 4 0.05 22 1.0 0.4 0Tuaran 105,435 90 0.26 56 0.4 1.3 0Total 3,206,742 43 0.48 50 0.5 25
296 *Rural urban locality data was included between 2011-2016; overall proportions were 0.48 urban, 0.44 rural and 0.08 unspecified. 297 # Beluran was formerly known as Labuk Sugut. ^ Nabawan was formerly known as Pensiangan. 298 ** Putatan and Tongod districts only commenced notifications in 2012.
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299 The changing annual spatial trend is shown in Fig 5, which shows high annual and mean
300 incidence rates often occurring in the western districts of Sabah. A shift in dynamics
301 occurred during the large 2015 outbreak, when incidence increased markedly in the more
302 densely populated western districts of Kota Kinabalu, Penampan, Putatan, and in Sandakan
303 and Semporna in the east (Fig 5). The overall urban case proportions in these districts ranged
304 from 53-85%. During 2016, cases from both urban and rural localities contributed to the
305 outbreak, but the greatest overall incidence was in Tenom, Nabawan and Keningau districts,
306 where the majority of cases were from rural localities.
307
308 Fig 5. Annual spatial incidence of dengue in Sabah, 2010-2016.
309 Incidence rates across districts are shown for each year, as well as the overall mean annual 310 incidence during the 7-year period. The 3 major cities of Sabah (Kota Kinabalu, Sandakan and 311 Tawau) are indicated by black circles.
312
313 Severe dengue
314 Of all dengue cases reported over the 7 years, 1.1% were severe (haemorrhagic) dengue
315 cases. The average annual state-wide number of severe cases was 18, although this
316 increased to 28 and 25 cases during 2011 and 2013, respectively, despite these being
317 relatively low incidence years (Figs 2, 6). The greatest proportion of severe cases were
318 concentrated in Sandakan (24%) and Tawau (20%) districts on the eastern side of the state,
319 with the highest severe dengue incidence found in Kunak, Sandakan and Tongod (Fig 6; Table
320 1). The lowest proportion and incidence of severe dengue was observed in the western
321 districts, several of which recorded zero severe cases, despite recording high overall dengue
322 incidence (Figs 5, 6; Table 1). Severe dengue occurred evenly across both genders and age
323 groups, although the burden was highest for age groups under 30 years, with the largest
324 proportion (35%) reported within the 10-19 years age group. There were 9 severe dengue
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325 deaths during the study period, 4 of which were in Tawau district. Deaths from severe
326 dengue occurred consistently across years, genders and age groups.
327
328 Fig 6. Total severe dengue notifications by district, 2010-2016.
329 Total number of severe dengue cases reported for each district during the 7-year period. The 330 3 major cities of Sabah (Kota Kinabalu, Sandakan and Tawau) are indicated.
331
332 Entomological factors
333 Entomological data collected within the 2015-2016 outbreaks indicated that the partially
334 sylvatic vector Aedes albopictus was the predominant species detected in larval collections
335 from both rural and urban case residences (Table 2). Of 719 dengue case residences that
336 were inspected as part of active surveillance in 2015-2016, 618 were found to contain
337 mosquito larvae (HI=86%). Of those, Ae. albopictus larvae were identified from 394
338 residences (HI=64%), either alone (383 residences) or with Ae. aegypti (11 residences).
339 Conversely, 94 residences were positive for Ae. aegypti (83 alone, 11 with Ae. Albopictus;
340 HI=15%), 33 were positive for Culex species (HI=5%), and 108 larval samples could not be
341 identified (17%).
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342 Table 2. Mosquito larvae species collected from case residences in Sabah during 2015-2016.
Number of larvae-positive residences with specific species present (HI)Locality
Total number of cases in
2015-2016
No. of case residences inspected
No. of larvae-positive case residences (all species)
House Index (HI) Ae.
aegyptiAe.
albopictusAe. aegypti &
Ae. albopictus* Culex spp. Undetermined
Urban residences 3,157 255 206 0.81 47 (0.23) 142 (0.69) 7 (0.03) 6 (0.03) 4 (0.02)Rural residences 2,824 437 388 0.89 33 (0.09) 225 (0.58) 4 (0.01) 26 (0.07) 100 (0.26)Locality unspecified 565 27 24 0.89 3 (0.13) 16 (0.67) 0 (0.0) 1 (0.04) 4 (0.17)Total 6,546 719 618 0.86 83 (0.13) 383 (0.62) 11 (0.02) 33 (0.05) 108 (0.17)
343 HI = proportion of residences positive for mosquito larvae, calculated as number of residences with larvae/number of residences inspected. 344 * Both species found breeding together in one household.345 Species were undetermined if the larvae failed to survive to adults to be identified, or if identification was pending/incomplete.
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346 The specific districts where case residences were inspected are detailed in S1 Table.
347 Inspections were conducted in 21/25 districts, though the majority were conducted in
348 Tawau (158 inspections; with 96 in urban localities) and Nabawan (107 inspections; all in
349 rural localities). The majority of residences positive for Ae. aegypti were in the east coast
350 districts of Tawau (larvae found in 60/158 residences) and Lahad Datu (18/43 larvae-positive
351 residences). Residences in Tawau and Lahad Datu districts together comprised 80% of all
352 Sabah residences where Ae. aegypti was identified. Ae. albopictus was prevalent across both
353 urban and rural residences of most of districts surveyed. The majority of residences positive
354 for Ae. albopictus larvae were also in Tawau (101/158 residences), followed by Penampang
355 (52/58 larvae-positive residences) and Nabawan and Keningau (46 residences each; S1
356 Table).
357
358 Discussion359360 Spatial and temporal trends
361 In recent years, the scale of epidemics in South East Asia has increased in both urban and
362 rural areas (28-32). We found an overall increasing incidence trend in the Malaysian state of
363 Sabah, with the highest risk period occurring annually between November and March. While
364 spatial trends in dengue incidence varied from year to year, the most intense transmission
365 across all years occurred in districts along the western coast of Sabah. The timing of large
366 epidemic years in Sabah (2010, 2015 and 2016) was consistent with patterns observed at
367 national and regional levels during the same period (wider Malaysia, Indonesia, Philippines)
368 (33, 34). This suggests shared seasonal influences on outbreak occurrence although,
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369 especially in the tropics, the causative relationship between temperature, rainfall and
370 transmission remains poorly understood (35, 36).
371 We also noted consistently high incidence in rural localities across the study period, as well
372 as a shift in the spatial dominance of urban versus rural localities during the large outbreaks
373 of 2015-2016. Our findings suggest that high density, urbanised areas are not necessarily the
374 primary drivers of ongoing epidemics in Sabah, and that factors other than population size
375 may drive the risk in rural areas. Recent studies in other areas of Malaysia have also shown
376 that dengue infection can occur at equivalent rates in both rural and urban areas (37, 38). It
377 could be that the threshold human density required to maintain transmission may be lower
378 than previously thought, although human movement (which may relate to population
379 density) between urban and rural areas is also likely to have influenced the patterns we
380 observed (39, 40).
381 Rural dominance of dengue has also been observed elsewhere in the region, including in
382 Cambodia, Thailand, Vietnam and Sri Lanka (41-44). These countries have all reported
383 epidemics spreading between rural and urban areas – in both directions – via human or
384 mosquito movement, facilitated by favourable climatic conditions. Other potential
385 influences on both urban and rural dengue transmission in Sabah might include water
386 storage practices, mosquito vector ecology and sociocultural factors (32, 45-48). The relative
387 importance of some of these risk factors in mediating dengue transmission is still not well
388 understood, even in urban areas (49, 50). Understanding the relationships between these
389 risk factors may be challenging to disentangle in different environments, and especially
390 where urban and rural areas are highly interconnected; however, knowledge of these
391 dynamics may be important to optimise the design and targeting of dengue control
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392 strategies. This is especially important given the potential for ongoing outbreaks in both
393 urban and rural areas of Sabah.
394 Demographic factors
395 Our findings indicated that the age-related dengue risk in Sabah was in line with regional
396 trends indicating a transition from children to adults being disproportionately affected by
397 dengue (51). Incidence was higher for males than for females across all districts of the state,
398 and was significantly higher for both genders in the 10-29 age group. This higher risk may
399 suggest that a larger proportion of people in this age-group (and possibly males in particular)
400 were either engaged in outdoor activities and/or being occupationally exposed. The
401 agriculture sector is the major employment sector in Sabah, and this type of work may
402 increase exposure to mosquitoes (52, 53). The particularly high proportion of males affected
403 in Tongod and Kinabatangan districts may reflect the fact that both are very rural, and may
404 have a larger proportion of men engaged in agricultural or recreational outdoor activities.
405 Outdoor activities, especially those in close proximity to forests or forest edges, are thought
406 to increase the risk of being bitten by the abundant exophilic vector, Ae. albopictus (54, 55).
407 However, studies in peninsula Malaysia have shown that Ae. albopictus can also adapt to
408 indoor urban environments (56, 57); hence, the high proportion of cases we observed in 10-
409 29 year olds could also suggest infection in indoor environments at home or at school.
410 Further investigation to identify the specific factors associated with infection risk in Sabah
411 may be useful to inform prevention strategies for this high-risk group.
412 Severe dengue
413 Changing demographic or immunological factors may also explain the observed pattern of
414 severe dengue in our study. Severe dengue showed a decreasing trend over time (despite
415 the overall increasing trend in incidence rates), but with the highest risk localised to two
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416 main regions of the state: the eastern districts of Tawau and Sandakan. These districts
417 include major urbanised cities as well as rural surrounding areas, and comprised relatively
418 low dengue rates compared to the west of the state. The reasons for this spatial
419 concentration of cases in these eastern districts is unknown, though it’s possible that a
420 serotype switch from DENV 4 to DENV 1 reported to have occurred in Sandakan between
421 2013 and 2016 may have contributed (12). It might also be possible that different areas of
422 Sabah experience serotype changes more or less frequently depending on levels and
423 direction of population movement (33, 58). Surveillance information regarding which virus
424 serotypes and genotypes were circulating in Sabah was not available in this study, so we
425 were unable to assess the potential contribution of virus circulation patterns to the trends
426 we observed. Assessing serological surveillance data alongside epidemiological data in future
427 studies in Sabah could aid predictions of severe disease risk (59, 60).
428 Entomological factors
429 During the large outbreak period between 2015 and 2016, our entomological surveillance
430 data indicated a striking association between the presence of the mosquito vector Ae.
431 albopictus relative to Ae. aegypti, in both urban and rural case residences in the majority of
432 the state. Interestingly, the eastern districts of Sabah state appeared to have a higher
433 proportion of Ae. aegypti compared to the rest of the state, although overall dengue
434 incidence was lower on the east coast. Interestingly, this finding was consistent with those of
435 early entomological surveys of Sabah in the 1970’s, which reported higher numbers of Ae.
436 aegypti on the east coast and lower abundance on the west coast (61, 62). In those studies,
437 the greater presence of Ae. aegypti in the east was thought to be due to more frequent
438 travel by boat between east coast settlements for fishing and trade.
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439 Although Ae. aegypti is generally considered responsible for most dengue transmission in
440 South East Asia (63, 64), Ae. albopictus is more common than Ae. aegypti across Malaysia
441 and Borneo (52, 54, 55, 65). Its competence for specific dengue genotypes, its abundance in
442 both rural and urban areas, its biting behaviour and its diverse aquatic habitat may all
443 account for patterns of mosquito-human contact and subsequent transmission in Sabah (48,
444 66). The presence of natural and artificial larval habitats for Ae. albopictus have previously
445 been associated with epidemic disease in both urban and rural areas of Malaysia (56, 57, 67)
446 despite the fact that globally, Ae. aegypti is undoubtedly the predominant vector driving
447 epidemics (68, 69). Urban dominance of Ae. albopictus has also been observed, at least
448 seasonally, in parts of Thailand, southern China and other South East Asian countries (70-72).
449 Given the likely role of Ae. albopictus in mediating dengue epidemics in Sabah, vector
450 control strategies may have to be expanded to include both Ae. aegypti and Ae. albopictus.
451 Because Ae. albopictus is commonly characterized as more exophagic and exophilic than Ae.
452 aegypti, and exploiting a wider range of hosts and habitats in peri-urban and rural
453 environments, targeting outdoor resting sites of adult Ae. albopictus may be a useful control
454 strategy in Sabah (73-75).
455 Limitations
456 The main caveat to our findings is that the changes in dengue case definition in Malaysia in
457 2014 may have influenced the trends reported here, in terms of either under- or over-
458 reporting of cases. Reduced reliance on clinical symptoms for case notification from 2014
459 onwards would be expected to reduce notifications dramatically but, in fact, a dramatic
460 increase in cases were recorded. It is possible that prior to that date, the lack of resources
461 for testing or notifying dengue, or other socioeconomic factors, may have resulted in under-
462 reporting (76). It is also possible that increases in diagnostic testing from 2014 were not
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463 uniform across all districts, and/or that additional reporting inconsistencies may have
464 impacted our observations.
465 Conclusions
466 The rising magnitude of dengue in Sabah in both rural and urban areas suggests that a better
467 understanding of dengue transmission across different environments is needed. Our findings
468 support the notion that dengue epidemics can be both urban and rural environment-driven,
469 and suggest risk factors that may be of use for clinicians, public health practitioners and
470 vector control teams. The trends observed in Sabah indicate that localized ecological,
471 human, virus and vector dynamics may be predictive of dengue epidemics irrespective of
472 urban and rural environment. In Sabah, as with many countries of South East Asia, there is
473 likely a complex interplay of these factors operating in both rural and urban areas, and these
474 probably overlap (10, 29). Considering the ongoing expansion of dengue endemicity and
475 burden in the region, proactive strategies to increase understanding of the complex and
476 evolving epidemiological factors underlying dengue risk across varied environments are
477 critical.
478
479 Acknowledgements 480
481 The authors would like to acknowledge the contribution of the Sabah Department of Health,
482 Ministry of Health, Malaysia for making dengue notification data available. We also thank
483 the Director General of Health Malaysia for the permission to publish this paper. We are
484 grateful for assistance and input provided by Nicholas Anstey, Matthew Grigg, Kimberley
485 Fornace, Christopher Wilkes, Eloise Stephenson and Andrea Rabellino. The author(s)
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486 received no specific funding for this work. The authors declare no conflict of interest in
487 conducting this study.
488
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673 72. Xu G, Dong H, Shi N, Liu S, Zhou A, Cheng Z, et al. An outbreak of dengue virus serotype 1 674 infection in Cixi, Ningbo, People's Republic of China, 2004, associated with a traveler from 675 Thailand and high density of Aedes albopictus. Am J Trop Med Hyg. 2007;76(6):1182-8.676 73. Muzari MO, Devine G, Davis J, Crunkhorn B, van den Hurk A, Whelan P, et al. Holding back the 677 tiger: Successful control program protects Australia from Aedes albopictus expansion. PLoS 678 Negl Trop Dis. 2017;11(2):e0005286.679 74. Delatte H, Paupy C, Dehecq JS, Thiria J, Failloux AB, Fontenille D. [Aedes albopictus, vector of 680 chikungunya and dengue viruses in Reunion Island: biology and control]. Parasite. 681 2008;15(1):3-13.682 75. Valerio L, Marini F, Bongiorno G, Facchinelli L, Pombi M, Caputo B, et al. Host-feeding patterns 683 of Aedes albopictus (Diptera: Culicidae) in urban and rural contexts within Rome province, 684 Italy. Vector Borne Zoonotic Dis. 2010;10(3):291-4.685 76. Beatty ME, Stone A, Fitzsimons DW, Hanna JN, Lam SK, Vong S, et al. Best practices in dengue 686 surveillance: a report from the Asia-Pacific and Americas Dengue Prevention Boards. PLoS 687 Negl Trop Dis. 2010;4(11):e890.
688
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689 Supporting Information690
691 S1 Fig. Seasonal decomposition of incidence rates in Sabah, 2010-2016.
692 The seasonal trend of dengue is shown in panel A, with the largest seasonal peak occurring on 693 average between Nov and May each year (indicated by vertical black lines). The additional 694 components separated from the seasonal trend during the decomposition procedure are also 695 indicated in panels B-D (cyclical component (B), irregular component (C) and overall smoothed 696 trend (D)).
697
698 S2 Fig. Variation in dengue incidence across Sabah districts, 2010-2016.
699 Dengue mean annual incidence rates across all years are plotted, in order of highest-lowest 700 mean annual incidence rate, showing the mean (x), median (line), and the range of rates (upper 701 and lower whiskers) across the years.
702
703 S1 Table. Entomological surveillance of case residences by district, 2015-2016.
704
705 S1 Checklist: STROBE Checklist for observational studies.
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