The effect of climate change on cholera disease: The road ahead using artificial neural network
Autoři:
Zahra Asadgol aff001; Hamed Mohammadi aff002; Majid Kermani aff001; Alireza Badirzadeh aff004; Mitra Gholami aff001
Působiště autorů:
Department of Environmental Health Engineering, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
aff001; Department of Environmental Health Engineering, School of Public Health, Zanjan University of Medical Sciences, Zanjan, Iran
aff002; Research Center for Environmental Health Technology, Iran University of Medical Sciences, Tehran, Iran
aff003; Department of Parasitology and Mycology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
aff004
Vyšlo v časopise:
PLoS ONE 14(11)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0224813
Souhrn
Climate change has been described to raise outbreaks of water-born infectious diseases and increases public health concerns. This study aimed at finding out these impacts on cholera infections by using Artificial Neural Networks (ANNs) from 2021 to 2050. Daily data for cholera infection cases in Qom city, which is located in the center of Iran, were analyzed from 1998 to 2016. To determine the best lag time and combination of inputs, Gamma Test (GT) was applied. General circulation model outputs were utilized to project future climate pattern under two scenarios of Representative Concentration Pathway (RCP2.6 and RCP8.5). Statistical downscaling was done to produce high-resolution synthetic time series weather dataset. ANNs were applied for simulating the impact of climate change on cholera. The observed climate variables including maximum and minimum temperatures and precipitation were tagged as predictors in ANNs. Cholera cases were considered as the target outcome variable. Projected future (2020–2050) climate in previous step was carried out to assess future cholera incidence. A seasonal trend in cholera infection was seen. Our results elucidated that the best lag time was 21 days. According to the results of downscaling tool, future climate in the study area by 2050 will be warmer and wetter. Simulation of cholera cases indicated that there is a clear trend of increasing cholera cases under the worst scenario (RCP8.5) by the year 2050 and the highest cholera cases observe in warmer months. The precipitation was recognized as the most effective input variable by sensitivity analysis. We observed a significant correlation between low precipitation and cholera infection. There is a strong evidence to show that cholera disease is correlated with environment variables, as low precipitation and high temperatures in warmer months could provide the swifter bacterial replication. These conditions in Iran, especially in the central parts, may raise the cholera infection rates. Furthermore, ANNs is an executive tool to simulate the impact of climate change on cholera to estimate the future trend of cholera incidence for adopting protective measures in endemic areas.
Klíčová slova:
Infectious diseases – Meteorology – Rain – Climate change – Weather – Artificial neural networks – Iran – Cholera
Zdroje
1. Semenza JC, Suk JE, Estevez V, Ebi KL, Lindgren E. Mapping climate change vulnerabilities to infectious diseases in Europe. Environmental health perspectives. 2011;120(3):385–92. doi: 10.1289/ehp.1103805 22113877
2. Greer A, Ng V, Fisman D. Climate change and infectious diseases in North America: the road ahead. Canadian Medical Association Journal. 2008;178(6):715–22. doi: 10.1503/cmaj.081325 18332386
3. Mills JN, Gage KL, Khan AS. Potential influence of climate change on vector-borne and zoonotic diseases: a review and proposed research plan. Environmental health perspectives. 2010;118(11):1507–14. doi: 10.1289/ehp.0901389 20576580
4. Tonnang HE, Kangalawe RY, Yanda PZ. Predicting and mapping malaria under climate change scenarios: the potential redistribution of malaria vectors in Africa. Malaria journal. 2010;9(1):111.
5. González C, Wang O, Strutz SE, González-Salazar C, Sánchez-Cordero V, Sarkar S. Climate change and risk of leishmaniasis in North America: predictions from ecological niche models of vector and reservoir species. PLoS neglected tropical diseases. 2010;4(1):e585. doi: 10.1371/journal.pntd.0000585 20098495
6. de Magny GC, Thiaw W, Kumar V, Manga NM, Diop BM, Gueye L, et al. Cholera outbreak in Senegal in 2005: was climate a factor? PLoS One. 2012;7(8):e44577. doi: 10.1371/journal.pone.0044577 22952995
7. Emch M, Feldacker C, Islam MS, Ali M. Seasonality of cholera from 1974 to 2005: a review of global patterns. International Journal of Health Geographics. 2008;7(1):31. doi: 10.1186/1476-072x-7-31 18570659
8. Weekly Epidemiological Record [Internet]. 21 September 2018.
9. Morens DM, Fauci AS. Emerging Infectious Diseases: Threats to Human Health and Global Stability. PLOS Pathogens. 2013;9(7):e1003467. doi: 10.1371/journal.ppat.1003467 23853589
10. Olago D, Marshall M, Wandiga SO, Opondo M, Yanda PZ, Kangalawe R, et al. Climatic, socio-economic, and health factors affecting human vulnerability to cholera in the Lake Victoria basin, East Africa. AMBIO: A Journal of the Human Environment. 2007;36(4):350–8.
11. Luque Fernández MÁ, Bauernfeind A, Jiménez JD, Gil CL, Omeiri NE, Guibert DH. Influence of temperature and rainfall on the evolution of cholera epidemics in Lusaka, Zambia, 2003–2006: analysis of a time series. Transactions of the Royal Society of Tropical Medicine and Hygiene. 2009;103(2):137–43. doi: 10.1016/j.trstmh.2008.07.017 18783808
12. Hashizume M, Armstrong B, Hajat S, Wagatsuma Y, Faruque AS, Hayashi T, et al. The effect of rainfall on the incidence of cholera in Bangladesh. Epidemiology. 2008;19(1):103–10. doi: 10.1097/EDE.0b013e31815c09ea 18091420
13. Ruiz-Moreno D, Pascual M, Bouma M, Dobson A, Cash B. Cholera seasonality in Madras (1901–1940): dual role for rainfall in endemic and epidemic regions. EcoHealth. 2007;4(1):52–62.
14. Hashizume M, Faruque AS, Wagatsuma Y, Hayashi T, Armstrong B. Cholera in Bangladesh:" Climatic Components of Seasonal Variation". Epidemiology. 2010:706–10. doi: 10.1097/EDE.0b013e3181e5b053 20562706
15. Lipp EK, Huq A, Colwell RR. Effects of global climate on infectious disease: the cholera model. Clinical microbiology reviews. 2002;15(4):757–70. doi: 10.1128/CMR.15.4.757-770.2002 12364378
16. Mohammadi H, Ardalan A, Bavani AM, Naddafi K, Talebian MTJHS. Simulation of Climate Change Impact on Emergency Medical Services Clients Caused by Air Pollution. 2018;7(2).
17. Nasr-Azadani F, Khan R, Rahimikollu J, Unnikrishnan A, Akanda A, Alam M, et al. Hydroclimatic sustainability assessment of changing climate on cholera in the Ganges-Brahmaputra basin. Advances in Water Resources. 2017;108:332–44.
18. de Magny GC, Murtugudde R, Sapiano MR, Nizam A, Brown CW, Busalacchi AJ, et al. Environmental signatures associated with cholera epidemics. Proceedings of the National Academy of Sciences. 2008;105(46):17676–81.
19. Pascual M, Chaves L, Cash B, Rodó X, Yunus M. Predicting endemic cholera: the role of climate variability and disease dynamics. Climate Research. 2008;36(2):131–40.
20. Akanda AS, Jutla AS, Gute DM, Evans T, Islam S. Reinforcing cholera intervention through prediction-aided prevention. Bulletin of the World Health Organization. 2012;90:243–4. doi: 10.2471/BLT.11.092189 22461722
21. Pezeshki Z, Tafazzoli-Shadpour M, Nejadgholi I, Mansourian A, Rahbar M. Model of cholera forecasting using artificial neural network in Chabahar City, Iran. Int J Enteric Pathog. 2016;4(1):1–8.
22. Penna MLF. Use of an artificial neural network for detecting excess deaths due to cholera in Ceará, Brazil. Revista de saude publica. 2004;38(3):351–7. doi: 10.1590/s0034-89102004000300003 15243663
23. climate-data. Available from: https://en.climate-data.org/asia/iran/qom/qom-956725/.
24. weather-atlas. Available from: https://www.weather-atlas.com/en/iran/qom-climate.
25. Koncar N. Optimisation methodologies for direct inverse neurocontrol: University of London; 1997.
26. Stefánsson A, Končar N, Jones AJJNC, Applications. A note on the gamma test. 1997;5(3):131–3.
27. Chuzhanova NA, Jones AJ, Margetts SJB. Feature selection for genetic sequence classification. 1998;14(2):139–43. doi: 10.1093/bioinformatics/14.2.139 9545445
28. Tsui APM. Smooth data modelling and stimulus-response via stabilisation of neural chaos: University of London; 1999.
29. Jones AJCUoW, Cardiff. The WinGamma User Guide. 1998;2001.
30. Moghaddamnia A, Gousheh MG, Piri J, Amin S, Han DJAiWR. Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. 2009;32(1):88–97.
31. Piri J, Amin S, Moghaddamnia A, Keshavarz A, Han D, Remesan RJJoHE. Daily pan evaporation modeling in a hot and dry climate. 2009;14(8):803–11.
32. Nasr-Azadani F, Unnikrishnan A, Akanda A, Islam S, Alam M, Huq A, et al. Downscaling river discharge to assess the effects of climate change on cholera outbreaks in the Bengal Delta. 2015;64(3):257–74.
33. Semenov MA, Stratonovitch PJCr. Use of multi-model ensembles from global climate models for assessment of climate change impacts. 2010;41(1):1–14.
34. Haris AA, Khan M, Chhabra V, Biswas S, Pratap A. Evaluation of LARS-WG for generating long term data for assessment of climate change impact in Bihar. 2010.
35. Yomwan P, Cao C, Rakwatin P, Suphamitmongkol W, Tian R, Saokarn AJG, Natural Hazards, et al. A study of waterborne diseases during flooding using Radarsat-2 imagery and a back propagation neural network algorithm. 2015;6(4):289–307.
36. HW B. The history of cholera in India from 1862 to 1881: Tru¨bner & Company; 1885.
37. Pascual M, Bouma MJ, Dobson AP. Cholera and climate: revisiting the quantitative evidence. Microbes and Infection. 2002;4(2):237–45. 11880057
38. Iacono GL, Armstrong B, Fleming LE, Elson R, Kovats S, Vardoulakis S, et al. Challenges in developing methods for quantifying the effects of weather and climate on water-associated diseases: A systematic review. PLoS neglected tropical diseases. 2017;11(6):e0005659. doi: 10.1371/journal.pntd.0005659 28604791
39. Martinez ME. The calendar of epidemics: Seasonal cycles of infectious diseases. PLoS pathogens. 2018;14(11):e1007327. doi: 10.1371/journal.ppat.1007327 30408114
40. Yue Y, Gong J, Wang D, Kan B, Li B, Ke C. Influence of climate factors on Vibrio cholerae dynamics in the Pearl River estuary, South China. World Journal of Microbiology and Biotechnology. 2014;30(6):1797–808. doi: 10.1007/s11274-014-1604-5 24442820
41. Mendelsohn J, Dawson T. Climate and cholera in KwaZulu-Natal, South Africa: the role of environmental factors and implications for epidemic preparedness. International journal of hygiene and environmental health. 2008;211(1–2):156–62. doi: 10.1016/j.ijheh.2006.12.002 17383231
42. Ramírez IJ. Cholera resurgence in Piura, Peru: examining climate associations during the 1997–1998 El Niño. GeoJournal. 2015;80(1):129–43.
43. WANG Z, Yao L, Guo Z, editors. The estimation of reference evapotranspiration based on gamma test and gene expression programming using the weather data set from different climatic zones in China. 2015 ASABE Annual International Meeting; 2015: American Society of Agricultural and Biological Engineers.
44. Bandyopadhyay S, Kanji S, Wang L. The impact of rainfall and temperature variation on diarrheal prevalence in Sub-Saharan Africa. Applied Geography. 2012;33:63–72.
45. Poveda G, Rojas W, Quiñones ML, Vélez ID, Mantilla RI, Ruiz D, et al. Coupling between annual and ENSO timescales in the malaria-climate association in Colombia. Environmental health perspectives. 2001;109(5):489–93. doi: 10.1289/ehp.01109489 11401760
46. Nasr-Azadani F, Unnikrishnan A, Akanda A, Islam S, Alam M, Huq A, et al. Downscaling river discharge to assess the effects of climate change on cholera outbreaks in the Bengal Delta. Climate Research. 2015;64(3):257–74.
47. Hartman J, Ebi K, McConnell KJ, Chan N, Weyant J. Climate suitability: for stable malaria transmission in Zimbabwe under different climate change scenarios. Global Change and Human Health. 2002;3(1):42.
48. Semenov MA, Stratonovitch P. Use of multi-model ensembles from global climate models for assessment of climate change impacts. Climate research. 2010;41(1):1–14.
49. Dibike YB, Coulibaly P. Hydrologic impact of climate change in the Saguenay watershed: comparison of downscaling methods and hydrologic models. Journal of hydrology. 2005;307(1–4):145–63.
50. Yomwan P, Cao C, Rakwatin P, Suphamitmongkol W, Tian R, Saokarn A. A study of waterborne diseases during flooding using Radarsat-2 imagery and a back propagation neural network algorithm. Geomatics, Natural Hazards and Risk. 2015;6(4):289–307.
51. Wang Y, Li J, Gu J, Zhou Z, Wang Z. Artificial neural networks for infectious diarrhea prediction using meteorological factors in Shanghai (China). Applied Soft Computing. 2015;35:280–90.
52. Organization WH. Protecting health from climate change: report for World Health Day 2008. 2008.
53. Bryden JL. Report on the General Aspects of Epidemic Cholera in 1869: A Sequel to A Report on the Cholera of 1866–68: Office of Superintendent of Government Print.; 1870.
54. Bryden JL. Epidemic Connection of the Cholera of Madras and Bombay with Cholera Epidemics of the Bengal Presidency: Office of Superintendent of Government Print.; 1871.
55. Acherjee B, Mondal S, Tudu B, Misra D. Application of artificial neural network for predicting weld quality in laser transmission welding of thermoplastics. Applied soft computing. 2011;11(2):2548–55.
56. V PS. Influence of environmental factors on the presence of Vibrio cholerae in the marine environment: a climate link. J Infect Dev Ctries. 2007;1:224–41. https://doi.org/10.3855/jidc.359. 19734600
57. Chowdhury FR, Nur Z, Hassan N, von Seidlein L, Dunachie S. Pandemics, pathogenicity and changing molecular epidemiology of cholera in the era of global warming. Annals of Clinical Microbiology and Antimicrobials. 2017;16(1):10. doi: 10.1186/s12941-017-0185-1 28270154
Článok vyšiel v časopise
PLOS One
2019 Číslo 11
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- Nejasný stín na plicích – kazuistika
- Masturbační chování žen v ČR − dotazníková studie
- Úspěšná resuscitativní thorakotomie v přednemocniční neodkladné péči
- Dlouhodobá recidiva a komplikace spojené s elektivní operací břišní kýly
Najčítanejšie v tomto čísle
- A daily diary study on maladaptive daydreaming, mind wandering, and sleep disturbances: Examining within-person and between-persons relations
- A 3’ UTR SNP rs885863, a cis-eQTL for the circadian gene VIPR2 and lincRNA 689, is associated with opioid addiction
- A substitution mutation in a conserved domain of mammalian acetate-dependent acetyl CoA synthetase 2 results in destabilized protein and impaired HIF-2 signaling
- Molecular validation of clinical Pantoea isolates identified by MALDI-TOF