Implications of the COVID-19 Lockdown on Dengue Transmission in Malaysia

Ong, Song‐Quan; Ngesom, Ahmad Mohiddin Mohd

doi:10.3390/idr13010016

Cited by 28 publications

(21 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The drastic measures imposed by the Sri Lanka government regarding COVID-19 outbreaks help the reduction of dengue hospitalisations. An identical pattern is also found during the first five phrases COVID-19 lockdown in Malaysia (Ong et al, 2021). A vector autoregression component (VAR; Spencer, 1993) can be added to our current setup to incorporate lagged effect dynamically from other variables.…”

Section: Discussionsupporting

confidence: 67%

Spatio-temporal detection for dengue outbreaks in the Central Region of Malaysia using climatic drivers at mesoscale and synoptic scale

Yip

Him

Jamil

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: The disease dengue is associated with both mesoscale and synoptic scale meteorology. Previous studies of this disease, especially for south-east Asia, have found very limited association between synoptic meteorological variables and the number of dengue hospitalisations. However, to tackle future severe outbreaks and to institute an early warning system, it will immensely beneficial to find and establish more clear association with rate of dengue hospitalisations and the most relevant meteorological variables. Objectives: A rigorous Bayesian modelling framework is provided to identify the most important meteorological covariates and their lagged effects for developing an early warning system for dengue outbreaks in the Central Region of Malaysia. Method: We obtain the dengue hospitalisations count data and the demographic information in the Central Region of Malaysia. Along with other mesoscale environmental measurements such as local temperature, precipitation and ozone concentration level, we also examine multiple synoptic scale Ni&ntildeo indices which are related to the phenomenon of El Ni&ntildeo Southern Oscillation and an unobserved meteorological variable derived from reanalysis data. A probabilistic early warning system is built based on a Bayesian spatio-temporal hierarchical model with a physically interpretable complex structure. Results: Our study finds a 46.87\% of increase in dengue hospitalisations due to one degree increase in the sea surface temperature anomalies in the central equatorial Pacific region with a lag time of six weeks. We also discover the existence of a mild association between the rate of cases and a distant lagged cooling effect of 28 weeks related to a phenomenon called El Ni&ntildeo Modoki. The proposed model also observes significant amount of association with some other meteorological parameters with disease rates. These associations are assessed by using an optimal Bayesian spatio-temporal model that outperforms other candidate models in terms of estimated out-of-sample predictive accuracy and performance in correctly issuing the warnings early. Discussion: With the novel spatial dynamic early warning system presented, our results show that the synoptic meteorological drivers can enhance short-term detection of dengue outbreaks and these can also potentially be used to provide longer-term forecasts

show abstract

Section: Discussionsupporting

confidence: 67%

Spatio-temporal detection for dengue outbreaks in the Central Region of Malaysia using climatic drivers at mesoscale and synoptic scale

Yip

Him

Jamil

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Reports on the impact of COVID-19 containment measures on the incidence of dengue have been variable, with increases in dengue incidence observed in Thailand and Singapore [ 20 , 21 ] and a decrease in Malaysia [ 15 ]. Data for the second quarter of 2020 suggested that the incidence of dengue decreased in Sri Lanka during that period of the COVID-19 pandemic [ 22 ].…”

Section: Discussionmentioning

confidence: 99%

“…An extension of ARIMA that supports the direct modeling of the seasonal component of the series is termed the Seasonal Autoregressive Integrated Moving Average model or SARIMA. The SARIMA model takes overall trends as well as seasonal changes in disease incidence into consideration [ 15 ]. Dengue incidence is seasonal and principally associated with rainfall in Sri Lanka [ 5 – 7 ].…”

Section: Methodsmentioning

confidence: 99%

“…Dengue incidence is seasonal and principally associated with rainfall in Sri Lanka [ 5 – 7 ]. Therefore, the anticipated numbers of monthly dengue cases from March 2020 to April 2021 were predicted using the SARIMA model from the recorded dengue case numbers for the corresponding months during the pre-pandemic period from January 2015 to February 2020 in a similar manner to a Malaysian study [ 15 ].…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Reduced dengue incidence during the COVID-19 movement restrictions in Sri Lanka from March 2020 to April 2021

et al. 2022

View full text Add to dashboard Cite

Background Dengue is the major mosquito-borne disease in Sri Lanka. After its first detection in January 2020, COVID-19 has become the major health issue in Sri Lanka. The impact of public health measures, notably restrictions on movement of people to curb COVID-19 transmission, on the incidence of dengue during the period March 2020 to April 2021 was investigated. Methods The incidence of dengue and COVID-19, rainfall and the public movement restrictions implemented to contain COVID-19 transmission were obtained from Sri Lanka government sources. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was used to predict the monthly dengue incidence from March 2020 to April 2021 for each of the country’s 25 districts based on five years of pre-pandemic data, and compared with the actual recorded incidence of dengue during this period. Ovitrap collections of Aedes larvae were performed in Jaffna city in the Jaffna district from August 2020 to April 2021 and the findings compared with similar collections made in the pre-pandemic period from March 2019 to December 2019. Results The recorded numbers of dengue cases for every month from March 2020 to April 2021 in the whole country and for all 25 districts over the same period were lower than the numbers of dengue cases predicted from data for the five years (2015–2019) immediately preceding the COVID-19 pandemic. The number of dengue cases recorded nationwide represented a 74% reduction from the predicted number of dengue cases for the March 2020 to April 2021 period. The numbers of Aedes larvae collected from ovitraps per month were reduced by 88.6% with a lower proportion of Ae. aegypti than Ae. albopictus in Jaffna city from August 2020 until April 2021 compared with March 2019 to December 2019. Conclusion Public health measures that restricted movement of people, closed schools, universities and offices to contain COVID-19 transmission unexpectedly led to a significant reduction in the reported numbers of dengue cases in Sri Lanka. This contrasts with findings reported from Singapore. The differences between the two tropical islands have significant implications for the epidemiology of dengue. Reduced access to blood meals and lower vector densities, particularly of Ae. aegypti, resulting from the restrictions on movement of people, are suggested to have contributed to the lower dengue incidence in Sri Lanka.

show abstract

“…A study of India found that the immature density of Aedes mosquito drastically increased during the COVID-19 lockdown due to paused vector control programs [4], and an increased density of vectors was also reported in Malaysia during the COVID-19 lockdown, which has led to increased dengue incidence [5]. During the lockdowns, when human movement is limited to and around own homes, perversely, human-vector contact may be enhanced, resulting in an increased risk of exposure and virus transmission.…”

mentioning

confidence: 99%