Author:
ProCite
RefWorks
Reference Manager
BibTeX
Zotero
EndNote
-
1.
World Health Organization , 2022. Dengue and Severe Dengue. Available at: https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue. Accessed June 2, 2023.
-
2.
World Health Organization , 2022. WHO Director-General’s Opening Remarks at Media Briefing – 2 November 2022. Available at: https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-media-briefing–2-november-2022. Accessed June 2, 2023.
-
3.
World Health Organization , 2022. WHA55.17 Dengue Fever and Dengue Haemorrhagic Fever Prevention and Control. Available at: https://www.who.int/publications/i/item/wha55.17. Accessed June 2, 2023.
-
4.
Kesetyaningsih TW , Andarini S , Sudarto S , Pramoedyo H , 2018. Determination of environmental factors affecting dengue incidence in Sleman District, Yogyakarta, Indonesia. Afr J Infect Dis 12: 13–25.
-
5.
Gutierrez JA , Laneri K , Aparicio JP , Sibona GJ , 2022. Meteorological indicators of dengue epidemics in non-endemic Northwest Argentina. Infect Dis Model 7: 823–834.
-
6.
Rungd-Ranzinger S , Horstick O , Marx M , Kroeger A , 2008. What does dengue disease surveillance contribute to predicting and detecting outbreaks and describing trends? Trop Med Int Health 13: 1022–1041.
-
7.
Murphy AK et al., 2022. Climate variability and Aedes vector indices in the southern Philippines: an empirical analysis. PLoS Negl Trop Dis 16: e0010478.
-
8.
Descloux E et al., 2012. Climate-based models for understanding and forecasting dengue epidemics. PLoS Negl Trop Dis 6: e1470.
-
9.
Koplewitz G , Lu F , Clemente L , Buckee C , Santillana M , 2022. Predicting dengue incidence leveraging internet-based data sources. A case study in 20 cities in Brazil. PLoS Negl Trop Dis 16: e0010071.
-
10.
Vu HH , Okumura J , Hashizume M , Tran DN , Yamamoto T , 2014. Regional differences in the growing incidence of dengue fever in Vietnam explained by weather variability. Trop Med Health 42: 25–33.
-
11.
Sia Su GL , 2008. Correlation of climatic factors and dengue incidence in Metro Manila, Philippines. Ambio 37: 292–294.
-
12.
Desjardins MR , Eastin MD , Paul R , Casas I , Delmelle EM , 2020. Space-time conditional autoregressive modeling to estimate neighborhood-level risks for dengue fever in Cali, Colombia. Am J Trop Med Hyg 103: 2040–2053.
-
13.
Baquero OS , Santana LMR , Chiaravalloti-Neto F , 2018. Dengue forecasting in São Paulo city with generalized additive models, artificial neural networks, and seasonal autoregressive integrated moving average models. PLoS One 13: e0195065.
-
14.
Bett B et al., 2019. Spatiotemporal analysis of historical records (2001–2012) on dengue fever in Vietnam and development of a statistical model for forecasting risk. PLoS One 14: e0224353.
-
15.
Wongkoon S , Jaroensutasinee M , Jaroensutasinee K , 2012. Development of temporal modeling for prediction of dengue infection in Northeastern Thailand. Asian Pac J Trop Med 5: 249–252.
-
16.
Aguiar M , Anam V , Blyuss KB , Estadilla CDS , Guerrero BV , Knopoff D , Kooi BW , Srivastav AK , Steindorf V , Stollenwerk N , 2022. Mathematical models for dengue fever epidemiology: a 10-year systematic review. Phys Life Rev 40: 65–92.
-
17.
Lowe R , Gasparrini A , Van Meerbeeck CJ , Lippi CA , Mahon R , Trotman AR , Rollock L , Hinds AQJ , Ryan SJ , Stewart-Ibarra AM , 2018. Nonlinear and delayed impacts of climate on dengue risk in Barbados: a modelling study. PLoS Med 15: e1002613.
-
18.
Xu L et al., 2017. Climate variation drives dengue dynamics. Proc Natl Acad Sci USA 114: 113–118.
-
19.
Cardenas R , Hussain-Alkhateeb L , Benitez-Valladares D , Sánchez-Tejeda G , Kroeger A , 2022. The Early Warning and Response System (EWARS-TDR) for dengue outbreaks: can it also be applied to chikungunya and Zika outbreak warning? BMC Infect Dis 22: 235.
-
20.
Colón-González FJ , Lake IR , Bentham G , 2011. Climate variability and dengue fever in warm and humid Mexico. Am J Trop Med Hyg 84: 757–763.
-
21.
Hii YL , Zhu H , Ng N , Ng LC , Rocklöv J , 2012. Forecast of dengue incidence using temperature and rainfall. PLoS Negl Trop Dis 6: e1908.
-
22.
Hettiarachchige C , von Cavallar S , Lynar T , Hickson RI , Gamvhir M , 2018. Risk prediction system for dengue transmission based on high resolution weather data. PLoS One 12: e0208203.
-
23.
Baak-Baak CM , Cigarroa-Toledo N , Pinto-Castillo JF , Cetina-Trejo RC , Torres-Chable O , Blitvich BJ , Garcia-Rejon JE , 2022. Cluster analysis of dengue morbidity and mortality in Mexico from 2007 to 2020: implications for the probable case definition. Am J Trop Med Hyg 106: 1515–1521.
-
24.
Chuang TW , Chaves LF , Chen PJ , 2017. Effects of local and regional climatic fluctuations on dengue outbreaks in southern Taiwan. PLoS One 12: e0178698.
-
25.
Zeng Z , Shan J , Chen L , Chen H , Cheng S , 2021. Global, regional, and national dengue burden from 1990 to 2017: a systematic analysis based on the global burden of disease study 2017. EClinicalMedicine 32: 100712.
-
26.
Undurraga EA et al., 2015. Economic and disease burden of dengue in Mexico. PLoS Negl Trop Dis 9: e0003547.
-
27.
Taborda A , Camorro C , Quintero J , Carrasquilla G , Londoño D , 2022. Cost-effectiveness of a dengue vector control intervention in Colombia. Am J Trop Med Hyg 107: 180–185.
-
28.
Edillo FE , Halasa FM , Erasmo JN , Amoin NB , Alera MT , Yoon IK , Alcantara AC , Shepard DS , 2015. Economic cost and burden of dengue in the Philippines. Am J Trop Med Hyg 92: 360–366.
-
29.
Kolimenakis A , Heinz S , Wilson ML , Winkler V , Yakob L , Michaelakis A , Papachristos D , Richardson C , Horstick O , 2021. The role of urbanization in the spread of Aedes mosquitoes and the diseases they transmit – a systematic review. PLoS Negl Trop Dis 15: e0009631.
-
30.
Lizarralde-Bejarano DP , Rojas-Diaz D , Arboleda-Sánchez S , Puerta-Yepes ME , 2020. Sensitivity, uncertainty and identifiability analyses to define a dengue transmission model with real data of an endemic municipality of Colombia. PLoS One 15: e0229668.
-
31.
Philippines Statistics Authority , 2023. ISSiP Inventory of Statistical Standards in the Philippines. Available at: https://psa.gov.ph/ISSiP/concepts-and-definitions/161175. Accessed June 2, 2023.
-
32.
CDC Mosquito Life Cycle: Aedes aegypti. Available at: www.cdc.gov/dengue. Accessed June 1, 2023.
-
33.
Biogents 2018. Life Cycle of Aedes Mosquitoes. Available at: https://sea.biogents.com/life-cycle-aedes-mosquitoes/. Accessed June 1, 2023.
-
34.
Edussuriya C , Deegalla S , Gawarammana I , 2021. An accurate mathematical model predicting number of dengue cases in tropics. PLoS Negl Trop Dis 15: e0009756.
-
35.
Ebi KL , Nealon J , 2016. Dengue in a changing climate. Environ Res 151: 115–123.
-
36.
de Oliveira HG , Barreto FR , Da Rosa ES , De Melo AS , De Arruda ME , Guimarães AE , 2014. Population density and dengue dispersion in urban areas. J Infect Dis 210: 670–671.
-
37.
Barrera R , Amador M , Acevedo V , Caban B , 2011. Status of dengue in Puerto Rico after the Zika epidemic. J Infect Dis 214 (Suppl 5 ):S297–S302.
-
38.
Cummings DA , Irizarry RA , Huang NE , Endy TP , Nisalak A , Ungchusak K , Burke DS , 2009. Travelling waves in the occurrence of dengue haemorrhagic fever in Thailand. Nature 427: 344–347.
-
39.
Colón-González FJ et al., 2021. Probabilistic seasonal dengue forecasting in Vietnam: a modelling study using superensembles. PLoS Med 18: e1003542.
-
40.
Buczak AL et al., 2014. Prediction of high incidence of dengue in the Philippines. PLoS Negl Trop Dis 8: e2771.
-
41.
Hau VH et al., 2022. Deep learning models for forecasting dengue fever based on climate data in Vietnam. PLoS Negl Trop Dis 16: e0010509.
-
42.
World Health Organization , 2016. Technical Handbook for Dengue Surveillance, Dengue Outbreak Prediction/Detection and Outbreak Response (“Model Contingency Plan”). Available at: https://fctc.who.int/publications/i/item/2016-09-30-technical-handbook-for-dengue-surveillance-dengue-outbreak-prediction-detection-and-outbreak-response. Accessed May 22, 2023.
-
43.
Agrupis KA , Ylade M , Aldaba J , Lopez AL , Deen J , 2019. Trends in dengue research in the Philippines: a systematic review. PLoS Negl Trop Dis 13: e0007280.
-
44.
Bravo L , Roque VG , Brett J , Dizon R , L’Azou M , 2014. Epidemiology of dengue disease in the Philippines (2000–2011): a systematic literature review. PLoS Negl Trop Dis 8: e3027.
-
45.
Sitchon J , 2022. Central Visayas Sees ‘Alarming’ Rise in Dengue Cases in First Half of 2022. Available at: https://www.rappler.com/nation/rise-dengue-cases-central-visayas-first-half-2022/. Accessed July 20, 2023.
-
46.
Magsumbol CN , 2022. Dengue Claims 71; Cases Soar to Over 11,000 in CV. Available at: https://www.philstar.com/the-freeman/cebu-news/2022/08/13/2202423/dengue-claims-71-cases-soar-over-11000-cv. Accessed July 20, 2023.
-
47.
Pineda-Cortel MRB , Clemente BM , Nga PTT , 2019. Modeling and predicting dengue fever cases in key regions of the Philippines using remote sensing data. Asian Pac J Trop Med 12: 60–66.
-
48.
Hossain S , Islam MM , Hasan MA , Chowdhury PB , Easty IA , Tusar MK , Rashid MB , Bashar K , 2023. Association of climate factors with dengue incidence in Bangladesh, Dhaka City: a count regression approach. Heliyon 9: e16053.
-
49.
Undurraga EA , Edillo FE , Erasmo JN , Alera MT , Yoon IK , Largo FM , Shepard DS , 2017. Disease burden of dengue in the Philippines: adjusting for underreporting by comparing active and passive dengue surveillance in Punta Princesa, Cebu City. Am J Trop Med Hyg 96: 887–898.
-
50.
Panja M , Chakraborty T , Nadim SS , Ghosh I , Kumar U , Liu N , 2023. An ensemble neural network approach to forecase dengue outbreak based on climatic condition. Chaos Solutions Fractals 167: 113124.
-
51.
Mai ST , Phi HT , Abubakar A , Kilpatrick P , Nguyen HQV , Vandierendonck H , 2023. Dengue fever: from extreme climates to outbreak prediction. 2022 IEEE International Conference on Data Mining proceedings, 1083–1088.
-
52.
Olmoguez ILG , Catindig MAC , Amongos MFL , Lazan FG , 2019. Developing a dengue forecasting model: a case study in Iligan City. Int J Adv Comput Sci Appl 10: 281–286.
-
53.
Subido ME , Aniversario IS , 2022. A correlation study between dengue incidence and climatological factors in the Philippines. Asian Res J Math 18: 110–119.
-
54.
Solidum JN , 2016. Correlation of climate change factors with dengue incidence in Old Balara, Quezon City, Philippines. IAMURE Int J Ecol Conserv 17: 113–122
-
55.
World Health Organization , 2020. Operational Guide Using the Web-Based Dashboard: Early Warning and Response System (EWARS) for Dengue Outbreaks, 2nd ed. Available at: https://apps.who.int/iris/handle/10665/332323. Accessed June 1, 2023.
-
56.
Ferdousi F , Yoshimatsu S , Ma E , Sohel N & Wagatsuma Y. 2015. Identification of essential containers for Aedes larval breeding to control dengue in Dhaka, Bangladesh. Trop Med Health 43: 253–264.
-
57.
Hermida MJ , Santangelo AP , Calero CI , Goizueta C , Espinosa M , Sigman M , 2021. Learning-by-teaching approach improves dengue knowledge in children and parents. Am J Trop Med Hyg 105: 1536–1543.
-
58.
Solidum JN , Solidum GG , 2015. Storytelling as a health teaching strategy for dengue prevention and control in the Philippines. Philipp J Sci 144: 61–67.
-
59.
Labrague LJ , 2013. Dengue knowledge and preventive practices among rural residents in Samar Province, Philippines. Am J Public Health Res 1: 47–52.
| Past two years | Past Year | Past 30 Days | |
|---|---|---|---|
| Abstract Views | 3966 | 1598 | 75 |
| Full Text Views | 68 | 31 | 0 |
| PDF Downloads | 77 | 30 | 0 |
Dengue in Cebu City, Philippines: A Pilot Study of Predictive Models and Visualizations for Public Health
Johnny Snyder
Johnny Snyder
Davis School of Business, Colorado Mesa University, Grand Junction, Colorado;
Search for other papers by Johnny Snyder in
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Search for other papers by Johnny Snyder in
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Gibson Maglasang
Gibson Maglasang
Research Institute for Computational Mathematics and Physics, Cebu Normal University, Cebu City, Philippines
Search for other papers by Gibson Maglasang in
Current site
Google Scholar
PubMed
Search for other papers by Gibson Maglasang in
Current site
Google Scholar
PubMed
ABSTRACT.
Dengue is a global health issue, particularly in the tropical and subtropical regions of the world. Prevention is the most appropriate method to fight the spread of the virus. The objective of this research is to present a model, along with visualizations, that will enable health officials and community leaders to identify when and where potential dengue outbreaks are likely to occur. Armed with this information, local resources can be adequately deployed in an effort to use limited supplies effectively. A mathematical model that uses easily obtainable data, along with visualizations for the 80 barangays of Cebu City, Philippines, is presented. Visualizations are constructed appropriate for a generalist audience to comprehend and use for dengue mitigation. Results of this study include a model that uses readily available data to predict dengue outbreaks one month in advance and visualizations appropriate for decision-makers in public health. Additional items are identified that could enhance the explanatory power of the model, and future directions are discussed.
Author Notes
Authors’ addresses: Johnny Snyder, Davis School of Business, Colorado Mesa University, Grand Junction, CO, E-mail: josnyder@coloradomesa.edu. Gibson Maglasang, Research Institute for Computational Mathematics and Physics, Cebu Normal University, Cebu City, Philippines, E-mail: maglasangg@cnu.edu.ph.
ProCite
RefWorks
Reference Manager
BibTeX
Zotero
EndNote
-
1.
World Health Organization , 2022. Dengue and Severe Dengue. Available at: https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue. Accessed June 2, 2023.
-
2.
World Health Organization , 2022. WHO Director-General’s Opening Remarks at Media Briefing – 2 November 2022. Available at: https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-media-briefing–2-november-2022. Accessed June 2, 2023.
-
3.
World Health Organization , 2022. WHA55.17 Dengue Fever and Dengue Haemorrhagic Fever Prevention and Control. Available at: https://www.who.int/publications/i/item/wha55.17. Accessed June 2, 2023.
-
4.
Kesetyaningsih TW , Andarini S , Sudarto S , Pramoedyo H , 2018. Determination of environmental factors affecting dengue incidence in Sleman District, Yogyakarta, Indonesia. Afr J Infect Dis 12: 13–25.
-
5.
Gutierrez JA , Laneri K , Aparicio JP , Sibona GJ , 2022. Meteorological indicators of dengue epidemics in non-endemic Northwest Argentina. Infect Dis Model 7: 823–834.
-
6.
Rungd-Ranzinger S , Horstick O , Marx M , Kroeger A , 2008. What does dengue disease surveillance contribute to predicting and detecting outbreaks and describing trends? Trop Med Int Health 13: 1022–1041.
-
7.
Murphy AK et al., 2022. Climate variability and Aedes vector indices in the southern Philippines: an empirical analysis. PLoS Negl Trop Dis 16: e0010478.
-
8.
Descloux E et al., 2012. Climate-based models for understanding and forecasting dengue epidemics. PLoS Negl Trop Dis 6: e1470.
-
9.
Koplewitz G , Lu F , Clemente L , Buckee C , Santillana M , 2022. Predicting dengue incidence leveraging internet-based data sources. A case study in 20 cities in Brazil. PLoS Negl Trop Dis 16: e0010071.
-
10.
Vu HH , Okumura J , Hashizume M , Tran DN , Yamamoto T , 2014. Regional differences in the growing incidence of dengue fever in Vietnam explained by weather variability. Trop Med Health 42: 25–33.
-
11.
Sia Su GL , 2008. Correlation of climatic factors and dengue incidence in Metro Manila, Philippines. Ambio 37: 292–294.
-
12.
Desjardins MR , Eastin MD , Paul R , Casas I , Delmelle EM , 2020. Space-time conditional autoregressive modeling to estimate neighborhood-level risks for dengue fever in Cali, Colombia. Am J Trop Med Hyg 103: 2040–2053.
-
13.
Baquero OS , Santana LMR , Chiaravalloti-Neto F , 2018. Dengue forecasting in São Paulo city with generalized additive models, artificial neural networks, and seasonal autoregressive integrated moving average models. PLoS One 13: e0195065.
-
14.
Bett B et al., 2019. Spatiotemporal analysis of historical records (2001–2012) on dengue fever in Vietnam and development of a statistical model for forecasting risk. PLoS One 14: e0224353.
-
15.
Wongkoon S , Jaroensutasinee M , Jaroensutasinee K , 2012. Development of temporal modeling for prediction of dengue infection in Northeastern Thailand. Asian Pac J Trop Med 5: 249–252.
-
16.
Aguiar M , Anam V , Blyuss KB , Estadilla CDS , Guerrero BV , Knopoff D , Kooi BW , Srivastav AK , Steindorf V , Stollenwerk N , 2022. Mathematical models for dengue fever epidemiology: a 10-year systematic review. Phys Life Rev 40: 65–92.
-
17.
Lowe R , Gasparrini A , Van Meerbeeck CJ , Lippi CA , Mahon R , Trotman AR , Rollock L , Hinds AQJ , Ryan SJ , Stewart-Ibarra AM , 2018. Nonlinear and delayed impacts of climate on dengue risk in Barbados: a modelling study. PLoS Med 15: e1002613.
-
18.
Xu L et al., 2017. Climate variation drives dengue dynamics. Proc Natl Acad Sci USA 114: 113–118.
-
19.
Cardenas R , Hussain-Alkhateeb L , Benitez-Valladares D , Sánchez-Tejeda G , Kroeger A , 2022. The Early Warning and Response System (EWARS-TDR) for dengue outbreaks: can it also be applied to chikungunya and Zika outbreak warning? BMC Infect Dis 22: 235.
-
20.
Colón-González FJ , Lake IR , Bentham G , 2011. Climate variability and dengue fever in warm and humid Mexico. Am J Trop Med Hyg 84: 757–763.
-
21.
Hii YL , Zhu H , Ng N , Ng LC , Rocklöv J , 2012. Forecast of dengue incidence using temperature and rainfall. PLoS Negl Trop Dis 6: e1908.
-
22.
Hettiarachchige C , von Cavallar S , Lynar T , Hickson RI , Gamvhir M , 2018. Risk prediction system for dengue transmission based on high resolution weather data. PLoS One 12: e0208203.
-
23.
Baak-Baak CM , Cigarroa-Toledo N , Pinto-Castillo JF , Cetina-Trejo RC , Torres-Chable O , Blitvich BJ , Garcia-Rejon JE , 2022. Cluster analysis of dengue morbidity and mortality in Mexico from 2007 to 2020: implications for the probable case definition. Am J Trop Med Hyg 106: 1515–1521.
-
24.
Chuang TW , Chaves LF , Chen PJ , 2017. Effects of local and regional climatic fluctuations on dengue outbreaks in southern Taiwan. PLoS One 12: e0178698.
-
25.
Zeng Z , Shan J , Chen L , Chen H , Cheng S , 2021. Global, regional, and national dengue burden from 1990 to 2017: a systematic analysis based on the global burden of disease study 2017. EClinicalMedicine 32: 100712.
-
26.
Undurraga EA et al., 2015. Economic and disease burden of dengue in Mexico. PLoS Negl Trop Dis 9: e0003547.
-
27.
Taborda A , Camorro C , Quintero J , Carrasquilla G , Londoño D , 2022. Cost-effectiveness of a dengue vector control intervention in Colombia. Am J Trop Med Hyg 107: 180–185.
-
28.
Edillo FE , Halasa FM , Erasmo JN , Amoin NB , Alera MT , Yoon IK , Alcantara AC , Shepard DS , 2015. Economic cost and burden of dengue in the Philippines. Am J Trop Med Hyg 92: 360–366.
-
29.
Kolimenakis A , Heinz S , Wilson ML , Winkler V , Yakob L , Michaelakis A , Papachristos D , Richardson C , Horstick O , 2021. The role of urbanization in the spread of Aedes mosquitoes and the diseases they transmit – a systematic review. PLoS Negl Trop Dis 15: e0009631.
-
30.
Lizarralde-Bejarano DP , Rojas-Diaz D , Arboleda-Sánchez S , Puerta-Yepes ME , 2020. Sensitivity, uncertainty and identifiability analyses to define a dengue transmission model with real data of an endemic municipality of Colombia. PLoS One 15: e0229668.
-
31.
Philippines Statistics Authority , 2023. ISSiP Inventory of Statistical Standards in the Philippines. Available at: https://psa.gov.ph/ISSiP/concepts-and-definitions/161175. Accessed June 2, 2023.
-
32.
CDC Mosquito Life Cycle: Aedes aegypti. Available at: www.cdc.gov/dengue. Accessed June 1, 2023.
-
33.
Biogents 2018. Life Cycle of Aedes Mosquitoes. Available at: https://sea.biogents.com/life-cycle-aedes-mosquitoes/. Accessed June 1, 2023.
-
34.
Edussuriya C , Deegalla S , Gawarammana I , 2021. An accurate mathematical model predicting number of dengue cases in tropics. PLoS Negl Trop Dis 15: e0009756.
-
35.
Ebi KL , Nealon J , 2016. Dengue in a changing climate. Environ Res 151: 115–123.
-
36.
de Oliveira HG , Barreto FR , Da Rosa ES , De Melo AS , De Arruda ME , Guimarães AE , 2014. Population density and dengue dispersion in urban areas. J Infect Dis 210: 670–671.
-
37.
Barrera R , Amador M , Acevedo V , Caban B , 2011. Status of dengue in Puerto Rico after the Zika epidemic. J Infect Dis 214 (Suppl 5 ):S297–S302.
-
38.
Cummings DA , Irizarry RA , Huang NE , Endy TP , Nisalak A , Ungchusak K , Burke DS , 2009. Travelling waves in the occurrence of dengue haemorrhagic fever in Thailand. Nature 427: 344–347.
-
39.
Colón-González FJ et al., 2021. Probabilistic seasonal dengue forecasting in Vietnam: a modelling study using superensembles. PLoS Med 18: e1003542.
-
40.
Buczak AL et al., 2014. Prediction of high incidence of dengue in the Philippines. PLoS Negl Trop Dis 8: e2771.
-
41.
Hau VH et al., 2022. Deep learning models for forecasting dengue fever based on climate data in Vietnam. PLoS Negl Trop Dis 16: e0010509.
-
42.
World Health Organization , 2016. Technical Handbook for Dengue Surveillance, Dengue Outbreak Prediction/Detection and Outbreak Response (“Model Contingency Plan”). Available at: https://fctc.who.int/publications/i/item/2016-09-30-technical-handbook-for-dengue-surveillance-dengue-outbreak-prediction-detection-and-outbreak-response. Accessed May 22, 2023.
-
43.
Agrupis KA , Ylade M , Aldaba J , Lopez AL , Deen J , 2019. Trends in dengue research in the Philippines: a systematic review. PLoS Negl Trop Dis 13: e0007280.
-
44.
Bravo L , Roque VG , Brett J , Dizon R , L’Azou M , 2014. Epidemiology of dengue disease in the Philippines (2000–2011): a systematic literature review. PLoS Negl Trop Dis 8: e3027.
-
45.
Sitchon J , 2022. Central Visayas Sees ‘Alarming’ Rise in Dengue Cases in First Half of 2022. Available at: https://www.rappler.com/nation/rise-dengue-cases-central-visayas-first-half-2022/. Accessed July 20, 2023.
-
46.
Magsumbol CN , 2022. Dengue Claims 71; Cases Soar to Over 11,000 in CV. Available at: https://www.philstar.com/the-freeman/cebu-news/2022/08/13/2202423/dengue-claims-71-cases-soar-over-11000-cv. Accessed July 20, 2023.
-
47.
Pineda-Cortel MRB , Clemente BM , Nga PTT , 2019. Modeling and predicting dengue fever cases in key regions of the Philippines using remote sensing data. Asian Pac J Trop Med 12: 60–66.
-
48.
Hossain S , Islam MM , Hasan MA , Chowdhury PB , Easty IA , Tusar MK , Rashid MB , Bashar K , 2023. Association of climate factors with dengue incidence in Bangladesh, Dhaka City: a count regression approach. Heliyon 9: e16053.
-
49.
Undurraga EA , Edillo FE , Erasmo JN , Alera MT , Yoon IK , Largo FM , Shepard DS , 2017. Disease burden of dengue in the Philippines: adjusting for underreporting by comparing active and passive dengue surveillance in Punta Princesa, Cebu City. Am J Trop Med Hyg 96: 887–898.
-
50.
Panja M , Chakraborty T , Nadim SS , Ghosh I , Kumar U , Liu N , 2023. An ensemble neural network approach to forecase dengue outbreak based on climatic condition. Chaos Solutions Fractals 167: 113124.
-
51.
Mai ST , Phi HT , Abubakar A , Kilpatrick P , Nguyen HQV , Vandierendonck H , 2023. Dengue fever: from extreme climates to outbreak prediction. 2022 IEEE International Conference on Data Mining proceedings, 1083–1088.
-
52.
Olmoguez ILG , Catindig MAC , Amongos MFL , Lazan FG , 2019. Developing a dengue forecasting model: a case study in Iligan City. Int J Adv Comput Sci Appl 10: 281–286.
-
53.
Subido ME , Aniversario IS , 2022. A correlation study between dengue incidence and climatological factors in the Philippines. Asian Res J Math 18: 110–119.
-
54.
Solidum JN , 2016. Correlation of climate change factors with dengue incidence in Old Balara, Quezon City, Philippines. IAMURE Int J Ecol Conserv 17: 113–122
-
55.
World Health Organization , 2020. Operational Guide Using the Web-Based Dashboard: Early Warning and Response System (EWARS) for Dengue Outbreaks, 2nd ed. Available at: https://apps.who.int/iris/handle/10665/332323. Accessed June 1, 2023.
-
56.
Ferdousi F , Yoshimatsu S , Ma E , Sohel N & Wagatsuma Y. 2015. Identification of essential containers for Aedes larval breeding to control dengue in Dhaka, Bangladesh. Trop Med Health 43: 253–264.
-
57.
Hermida MJ , Santangelo AP , Calero CI , Goizueta C , Espinosa M , Sigman M , 2021. Learning-by-teaching approach improves dengue knowledge in children and parents. Am J Trop Med Hyg 105: 1536–1543.
-
58.
Solidum JN , Solidum GG , 2015. Storytelling as a health teaching strategy for dengue prevention and control in the Philippines. Philipp J Sci 144: 61–67.
-
59.
Labrague LJ , 2013. Dengue knowledge and preventive practices among rural residents in Samar Province, Philippines. Am J Public Health Res 1: 47–52.
| Past two years | Past Year | Past 30 Days | |
|---|---|---|---|
| Abstract Views | 3966 | 1598 | 75 |
| Full Text Views | 68 | 31 | 0 |
| PDF Downloads | 77 | 30 | 0 |