ISSN: 3108-0723 (Online)

International Journal of Technology, Health and Sustainability

IF: 2.64

as on 30st September, 2026.

International Journal of Technology, Health and Sustainability,

Host Ecology, Environmental Change, and Infectious Disease Transmission Dynamics: A Multidisciplinary Modelling Perspective

Moshood Ayinde1, Habeeb Damilola Yusuf2, Kazeem Abdulrazaq3

1 Trinity Health, IA, USA.

2 Owen Graduate School of Management, Vanderbilt University, Nashville, Tennessee, USA.

3 Department of Public Health Sciences, New Mexico State University, Las Cruces, New Mexico, USA.

Abstract

Models of infectious disease transmission inherited a structure from Ross and Macdonald that assumes homogeneous biting, well-mixed host populations, age-independent vector mortality and a single vector species. Every substantial advance in the field since has consisted of relaxing one of those assumptions, and the relaxations have not been independent: host heterogeneity, spatial structure, thermal dependence and human mobility interact, so that a model correcting one assumption while retaining the others can be worse calibrated than the original. This review examines what host ecology and environmental change have contributed to transmission modelling, and what the resulting models can and cannot support. It argues four positions. First, host competence is properly treated as a continuous, condition-dependent organismal trait rather than a species-level constant, and when it is, transmission in multi-host systems is found to be dominated by a small number of highly competent and preferentially fed-upon species — making community-mean quantities close to uninformative. Second, the biodiversity–disease relationship is not a general law in either direction; the current synthesis makes the direction a testable prediction from host traits and community assembly rules, and the largest available meta-analysis finds biodiversity loss, pollution, climate change and introduced species associated with increased disease while urbanisation is associated with decreased disease and natural biodiversity gradients are comparatively unimportant. Third, thermal-performance modelling has changed the sign of projected warming effects in warm regions by establishing that transmission optima are unimodal and substantially lower than earlier models assumed, with temperate mosquito-borne virus transmission peaking between 23°C and 26°C and malaria near 25°C. Fourth, model credibility now rests on forecast evaluation rather than on fit, and the honest results of open forecasting challenges — in which many submitted models did not clearly outperform historical baselines — should temper claims about predictive capability. The review concludes with a research agenda emphasising mechanism-specific hypotheses, proper scoring of probabilistic forecasts, explicit treatment of surveillance bias and detection probability, and coupling of ecological models to behavioural, economic and equity components rather than adjustment for them as confounders.

Keywords: Transmission dynamics, Host competence, Reservoir ecology, Dilution effect, Thermal biology, Species distribution models, Forecast evaluation, One Health, Land-use change

Download PDF

References

  1. Adaramola, T.S., Fadero, S. and Gideon, E.N. (2020) ‘Machine learning and data-driven methods for industrial automation and process control’, International Journal of Engineering and Modern Technology, 6(3), pp. 103–128. https://doi.org/10.56201/ijemt.vol.6.no3.2020.pg103.128
  2. Akanbi, O. and Sunday, E.A. (2024) ‘An integrated path-planning and slotting optimization model for AMR-enabled high-density warehousing’, International Journal of Advanced Multidisciplinary Research and Studies, 4(6), pp. 3226–3243. https://doi.org/10.62225/2583049X.2024.4.6.6181
  3. Akanbi, O. and Sunday, E.A. (2025) ‘A systematic review of AI-driven autonomous mobile robots (AMR) in scalable micro-fulfillment centers’, International Journal of Advanced Multidisciplinary Research and Studies, 5(6), pp. 2363–2379. https://doi.org/10.62225/2583049X.2025.5.6.6182
  4. Akanbi, O., Ganiu, O.S. and Sunday, E.A. (2025) ‘A multi-agent AI framework for swarm intelligence in autonomous mobile robot (AMR) fleet coordination’, International Journal of Scientific Research in Humanities and Social Sciences, 2(1), pp. 81–109.
  5. Allan, B.F., Keesing, F. and Ostfeld, R.S. (2003) ‘Effect of forest fragmentation on Lyme disease risk’, Conservation Biology, 17(1), pp. 267–272. https://doi.org/10.1046/j.1523-1739.2003.01260.x
  6. Amos, B., Aurrecoechea, C., Barba, M., et al. (2022) ‘VEuPathDB: the eukaryotic pathogen, vector and host bioinformatics resource center’, Nucleic Acids Research, 50(D1), pp. D898–D911. https://doi.org/10.1093/nar/gkab929
  7. Anyamba, A., Chretien, J.-P., Small, J., et al. (2009) ‘Prediction of a Rift Valley fever outbreak’, Proceedings of the National Academy of Sciences, 106(3), pp. 955–959. https://doi.org/10.1073/pnas.0806490106
  8. Ayinde, M., Ojukwu, P.U. and Ohunyon, G. (2025) ‘Organizational readiness for generative AI integration in healthcare operations: comparative management capabilities between the U.S. and low- and middle-income countries’, International Journal of Advanced Multidisciplinary Research and Studies, 5(6), pp. 2207–2222.
  9. Balcan, D., Colizza, V., Goncalves, B., et al. (2009) ‘Multiscale mobility networks and the spatial spreading of infectious diseases’, Proceedings of the National Academy of Sciences, 106(51), pp. 21484–21489. https://doi.org/10.1073/pnas.0906910106
  10. Becker, D.J., Streicker, D.G. and Altizer, S. (2015) ‘Linking anthropogenic resources to wildlife–pathogen dynamics: a review and meta-analysis’, Ecology Letters, 18(5), pp. 483–495. https://doi.org/10.1111/ele.12428
  11. Beven, K. and Freer, J. (2001) ‘Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology’, Journal of Hydrology, 249(1–4), pp. 11–29. https://doi.org/10.1016/S0022-1694(01)00421-8
  12. Bhatt, S., Gething, P.W., Brady, O.J., et al. (2013) ‘The global distribution and burden of dengue’, Nature, 496(7446), pp. 504–507. https://doi.org/10.1038/nature12060
  13. Bhatt, S., Weiss, D.J., Cameron, E., et al. (2015) ‘The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015’, Nature, 526(7572), pp. 207–211. https://doi.org/10.1038/nature15535
  14. Biswal, S., Reynales, H., Saez-Llorens, X., et al. (2019) ‘Efficacy of a tetravalent dengue vaccine in healthy children and adolescents’, New England Journal of Medicine, 381(21), pp. 2009–2019. https://doi.org/10.1056/NEJMoa1903869
  15. Bracher, J., Ray, E.L., Gneiting, T., et al. (2021) ‘Evaluating epidemic forecasts in an interval format’, PLOS Computational Biology, 17(2), e1008618. https://doi.org/10.1371/journal.pcbi.1008618
  16. Brady, O.J., Kharisma, D.D., Wilastonogoro, N.N., et al. (2020) ‘The cost-effectiveness of controlling dengue in Indonesia using wMel Wolbachia released at scale: a modelling study’, BMC Medicine, 18(1), 186. https://doi.org/10.1186/s12916-020-01638-2
  17. Carlson, C.J., Albery, G.F., Merow, C., et al. (2022) ‘Climate change increases cross-species viral transmission risk’, Nature, 607(7919), pp. 555–562. https://doi.org/10.1038/s41586-022-04788-w
  18. Cazelles, B., Chavez, M., McMichael, A.J., et al. (2005) ‘Nonstationary influence of El Niño on the synchronous dengue epidemics in Thailand’, PLoS Medicine, 2(4), e106. https://doi.org/10.1371/journal.pmed.0020106
  19. Cecilia, H., Drouin, A., Metras, R., et al. (2022) ‘Mechanistic models of Rift Valley fever virus transmission: a systematic review’, PLOS Neglected Tropical Diseases, 16(11), e0010339. https://doi.org/10.1371/journal.pntd.0010339
  20. Childs, M.L., Lyberger, K., Harris, M.J., et al. (2025) ‘Climate warming is expanding dengue burden in the Americas and Asia’, Proceedings of the National Academy of Sciences, 122(37), e2512350122. https://doi.org/10.1073/pnas.2512350122
  21. Civitello, D.J., Cohen, J., Fatima, H., et al. (2015) ‘Biodiversity inhibits parasites: broad evidence for the dilution effect’, Proceedings of the National Academy of Sciences, 112(28), pp. 8667–8671. https://doi.org/10.1073/pnas.1506279112
  22. Colón-González, F.J., Sewe, M.O., Tompkins, A.M., et al. (2021) ‘Projecting the risk of mosquito-borne diseases in a warmer and more populated world: a multi-model, multi-scenario intercomparison modelling study’, The Lancet Planetary Health, 5(7), e404–e414. https://doi.org/10.1016/S2542-5196(21)00132-7
  23. Costa, E.de-F., Streng, K., Santos, M.Ade-S., et al. (2024) ‘The effect of temperature on the boundary conditions of West Nile virus circulation in Europe’, PLOS Neglected Tropical Diseases, 18(5), e0012162. https://doi.org/10.1371/journal.pntd.0012162
  24. Cross, P.C., Prosser, D.J., Ramey, A.M., et al. (2019) ‘Confronting models with data: the challenges of estimating disease spillover’, Philosophical Transactions of the Royal Society B: Biological Sciences, 374(1782), 20180435. https://doi.org/10.1098/rstb.2018.0435
  25. Datoo, M.S., Dicko, A., Tinto, H., et al. (2024) ‘Safety and efficacy of malaria vaccine candidate R21/Matrix-M in African children: a multicentre, double-blind, randomised, phase 3 trial’, The Lancet, 403(10426), pp. 533–544. https://doi.org/10.1016/S0140-6736(23)02511-4
  26. Diekmann, O., Heesterbeek, J.A.P. and Metz, J.A.J. (1990) ‘On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations’, Journal of Mathematical Biology, 28(4), pp. 365–382. https://doi.org/10.1007/BF00178324
  27. Diekmann, O., Heesterbeek, J.A.P. and Roberts, M.G. (2010) ‘The construction of next-generation matrices for compartmental epidemic models’, Journal of the Royal Society Interface, 7(47), pp. 873–885. https://doi.org/10.1098/rsif.2009.0386
  28. Diuk-Wasser, M.A., VanAcker, M.C. and Fernandez, M.P. (2021) ‘Impact of land use changes and habitat fragmentation on the eco-epidemiology of tick-borne diseases’, Journal of Medical Entomology, 58(4), pp. 1546–1564. https://doi.org/10.1093/jme/tjaa209
  29. Dolan, M.C., Maupin, G.O., Schneider, B.S., et al. (2004) ‘Control of immature Ixodes scapularis (Acari: Ixodidae) on rodent reservoirs of Borrelia burgdorferi in a residential community of southeastern Connecticut’, Journal of Medical Entomology, 41(6), pp. 1043–1054. https://doi.org/10.1603/0022-2585-41.6.1043
  30. Elith, J., Phillips, S.J., Hastie, T., et al. (2011) ‘A statistical explanation of MaxEnt for ecologists’, Diversity and Distributions, 17(1), pp. 43–57. https://doi.org/10.1111/j.1472-4642.2010.00725.x
  31. Erazo, D., Grant, L., Ghisbain, G., et al. (2024) ‘Contribution of climate change to the spatial expansion of West Nile virus in Europe’, Nature Communications, 15(1), 1196. https://doi.org/10.1038/s41467-024-45290-3
  32. Eubank, S., Guclu, H., Kumar, V.S.A., et al. (2004) ‘Modelling disease outbreaks in realistic urban social networks’, Nature, 429(6988), pp. 180–184. https://doi.org/10.1038/nature02541
  33. Farooq, Z., Rocklöv, J., Wallin, J., et al. (2022) ‘Artificial intelligence to predict West Nile virus outbreaks with eco-climatic drivers’, The Lancet Regional Health – Europe, 17, 100370. https://doi.org/10.1016/j.lanepe.2022.100370
  34. Faust, C.L., McCallum, H.I., Bloomfield, L.S.P., et al. (2018) ‘Pathogen spillover during land conversion’, Ecology Letters, 21(4), pp. 471–483. https://doi.org/10.1111/ele.12904
  35. Ferguson, N.M. (2007) ‘Capturing human behaviour’, Nature, 446(7137), p. 733. https://doi.org/10.1038/446733a
  36. Ferguson, N.M., Kien, D.T.H., Clapham, H., et al. (2015) ‘Modeling the impact on virus transmission of Wolbachia-mediated blocking of dengue virus infection of Aedes aegypti’, Science Translational Medicine, 7(279), 279ra37. https://doi.org/10.1126/scitranslmed.3010370
  37. Funk, S., Camacho, A., Kucharski, A.J., et al. (2019) ‘Assessing the performance of real-time epidemic forecasts: a case study of Ebola in the Western Area region of Sierra Leone, 2014–15’, PLOS Computational Biology, 15(2), e1006785. https://doi.org/10.1371/journal.pcbi.1006785
  38. Funk, S., Gilad, E. and Jansen, V.A.A. (2010) ‘Endemic disease, awareness, and local behavioural response’, Journal of Theoretical Biology, 264(2), pp. 501–509. https://doi.org/10.1016/j.jtbi.2010.02.032
  39. Garrett-Jones, C. (1964) ‘Prognosis for interruption of malaria transmission through assessment of the mosquito’s vectorial capacity’, Nature, 204(4964), pp. 1173–1175. https://doi.org/10.1038/2041173a0
  40. Gibb, R., Albery, G.F., Becker, D.J., et al. (2021) ‘Data proliferation, reconciliation, and synthesis in viral ecology’, BioScience, 71(11), pp. 1148–1156. https://doi.org/10.1093/biosci/biab080
  41. Gibb, R., Redding, D.W., Chin, K.Q., et al. (2020) ‘Zoonotic host diversity increases in human-dominated ecosystems’, Nature, 584(7821), pp. 398–402. https://doi.org/10.1038/s41586-020-2562-8
  42. Gibb, R., Redding, D.W., Friant, S., et al. (2025) ‘Towards a “people and nature” paradigm for biodiversity and infectious disease’, Philosophical Transactions of the Royal Society B: Biological Sciences, 380(1917), 20230259. https://doi.org/10.1098/rstb.2023.0259
  43. Gideon, E.N., Adaramola, T.S. and Fadero, S. (2019) ‘Reliability engineering and failure analysis in complex engineering systems’, IRE Journals, 2(11), pp. 703–727. https://doi.org/10.64388/IREV2111-1722490
  44. Global Consortium for H5N8 and Related Influenza Viruses (2016) ‘Role for migratory wild birds in the global spread of avian influenza H5N8’, Science, 354(6309), pp. 213–217. https://doi.org/10.1126/science.aaf8852
  45. Gneiting, T. and Raftery, A.E. (2007) ‘Strictly proper scoring rules, prediction, and estimation’, Journal of the American Statistical Association, 102(477), pp. 359–378. https://doi.org/10.1198/016214506000001437
  46. Grassly, N.C. and Fraser, C. (2008) ‘Mathematical models of infectious disease transmission’, Nature Reviews Microbiology, 6(6), pp. 477–487. https://doi.org/10.1038/nrmicro1845
  47. Halsey, S. (2019) ‘Defuse the dilution effect debate’, Nature Ecology & Evolution, 3(2), pp. 145–146. https://doi.org/10.1038/s41559-018-0764-3
  48. Hamer, G.L., Kitron, U.D., Goldberg, T.L., et al. (2009) ‘Host selection by Culex pipiens mosquitoes and West Nile virus amplification’, American Journal of Tropical Medicine and Hygiene, 80(2), pp. 268–278. https://doi.org/10.4269/ajtmh.2009.80.268
  49. Han, B.A., Schmidt, J.P., Bowden, S.E., et al. (2015) ‘Rodent reservoirs of future zoonotic diseases’, Proceedings of the National Academy of Sciences, 112(22), pp. 7039–7044. https://doi.org/10.1073/pnas.1501598112
  50. Hart, W.S., Hurrell, J.W., Kaye, A.R., et al. (2025) ‘Climate variability amplifies the need for vector-borne disease outbreak preparedness’, Proceedings of the National Academy of Sciences, 122(34), e2507311122. https://doi.org/10.1073/pnas.2507311122
  51. Hassell, J.M., Begon, M., Ward, M.J., et al. (2017) ‘Urbanization and disease emergence: dynamics at the wildlife–livestock–human interface’, Trends in Ecology & Evolution, 32(1), pp. 55–67. https://doi.org/10.1016/j.tree.2016.09.012
  52. Haydon, D.T., Cleaveland, S., Taylor, L.H., et al. (2002) ‘Identifying reservoirs of infection: a conceptual and practical challenge’, Emerging Infectious Diseases, 8(12), pp. 1468–1473. https://doi.org/10.3201/eid0812.010317
  53. Heberling, J.M., Miller, J.T., Noesgaard, D., et al. (2021) ‘Data integration enables global biodiversity synthesis’, Proceedings of the National Academy of Sciences, 118(6), e2018093118. https://doi.org/10.1073/pnas.2018093118
  54. Hoffmann, A.A., Montgomery, B.L., Popovici, J., et al. (2011) ‘Successful establishment of Wolbachia in Aedes populations to suppress dengue transmission’, Nature, 476(7361), pp. 454–457. https://doi.org/10.1038/nature10356
  55. Höhle, M. and an der Heiden, M. (2014) ‘Bayesian nowcasting during the STEC O104:H4 outbreak in Germany, 2011’, Biometrics, 70(4), pp. 993–1002. https://doi.org/10.1111/biom.12194
  56. Holcomb, K.M., Mathis, S., Staples, J.E., et al. (2023) ‘Evaluation of an open forecasting challenge to assess skill of West Nile virus neuroinvasive disease prediction’, Parasites & Vectors, 16(1), 11. https://doi.org/10.1186/s13071-022-05630-y
  57. Hoover, C.M., Sokolow, S.H., Kemp, J., et al. (2019) ‘Modelled effects of prawn aquaculture on poverty alleviation and schistosomiasis control’, Nature Sustainability, 2(7), pp. 611–620. https://doi.org/10.1038/s41893-019-0301-7
  58. Huber, J.H., Childs, M.L., Caldwell, J.M., et al. (2018) ‘Seasonal temperature variation influences climate suitability for dengue, chikungunya, and Zika transmission’, PLOS Neglected Tropical Diseases, 12(5), e0006451. https://doi.org/10.1371/journal.pntd.0006451
  59. Ike, P.N., Aifuwa, S.E., Nnabueze, S.B., et al. (2024) ‘Quantitative risk architecture for public–private partnerships: a multi-layered model for allocating public and private risk’, International Journal of Advanced Multidisciplinary Research and Studies, 4(6), pp. 2669–2682. https://doi.org/10.62225/2583049X.2024.4.6.5021
  60. Ike, P.N., Okojie, J.S., Nnabueze, S.B., et al. (2025) ‘Digital twin-driven environmental compliance models for sustainable procurement in oil, gas, and utilities’, International Journal of Advanced Multidisciplinary Research and Studies, 5(5), pp. 562–576.
  61. IPCC (2022) Climate change 2022: impacts, adaptation and vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. https://doi.org/10.1017/9781009325844
  62. Johansson, M.A., Apfeldorf, K.M., Dobson, S., et al. (2019) ‘An open challenge to advance probabilistic forecasting for dengue epidemics’, Proceedings of the National Academy of Sciences, 116(48), pp. 24268–24274. https://doi.org/10.1073/pnas.1909865116
  63. Joseph, M.B., Mihaljevic, J.R., Orlofske, S.A., et al. (2013) ‘Does life history mediate changing disease risk when communities disassemble?’, Ecology Letters, 16(11), pp. 1405–1412. https://doi.org/10.1111/ele.12180
  64. Joseph, O.B., Ijiga, O.M., Olateji, M., et al. (2025b) ‘Comparative perspectives on TVET: lessons from the United States and developing economies for workforce readiness and economic inclusion’, International Journal of Advanced Multidisciplinary Research and Studies, 5(2), pp. 2493–2506.
  65. Joseph, O.B., Olateji, M., Ijiga, O.M., et al. (2025a) ‘Future-proofing skills in the Global South: strategic directions for transforming technical and vocational education and training (TVET)’, International Journal of Advanced Multidisciplinary Research and Studies, 5(2), pp. 2478–2492. https://doi.org/10.62225/2583049X.2025.5.2.4730
  66. Joseph, O.B., Olateji, M., Okoli, I., et al. (2025c) ‘Policy, power, and performance: the role of government in shaping successful TVET outcomes — a cross-national study’, International Journal of Advanced Multidisciplinary Research and Studies, 5(2), pp. 2507–2521. https://doi.org/10.62225/2583049X.2025.5.2.4732
  67. Kaye, A.R., Obolski, U., Sun, L., et al. (2024) ‘The impact of natural climate variability on the global distribution of Aedes aegypti: a mathematical modelling study’, The Lancet Planetary Health, 8(12), e1079–e1087. https://doi.org/10.1016/S2542-5196(24)00238-9
  68. Keeling, M.J. and Rohani, P. (2008) Modeling infectious diseases in humans and animals. 1st ed. Princeton, NJ: Princeton University Press. https://doi.org/10.1515/9781400841035
  69. Keesing, F., Belden, L.K., Daszak, P., et al. (2010) ‘Impacts of biodiversity on the emergence and transmission of infectious diseases’, Nature, 468(7324), pp. 647–652. https://doi.org/10.1038/nature09575
  70. Kianguebene-Koussingounina, C.E., Olajide-Kayode, J.O., Oyediran, I.A., et al. (2023) ‘Geostatistical assessment of soils in Ibadan, Southwest Nigeria: focus on agricultural lands’, Environmental and Sustainability Indicators, 19, 100287. https://doi.org/10.1016/j.indc.2023.100287
  71. Kilpatrick, A.M. and Randolph, S.E. (2012) ‘Drivers, dynamics, and control of emerging vector-borne zoonotic diseases’, The Lancet, 380(9857), pp. 1946–1955. https://doi.org/10.1016/S0140-6736(12)61151-9
  72. Kilpatrick, A.M., Daszak, P., Jones, M.J., et al. (2006a) ‘Host heterogeneity dominates West Nile virus transmission’, Proceedings of the Royal Society B: Biological Sciences, 273(1599), pp. 2327–2333. https://doi.org/10.1098/rspb.2006.3575
  73. Kilpatrick, A.M., Kramer, L.D., Jones, M.J., et al. (2006b) ‘West Nile virus epidemics in North America are driven by shifts in mosquito feeding behavior’, PLoS Biology, 4(4), e82. https://doi.org/10.1371/journal.pbio.0040082
  74. Kilpatrick, H.J., LaBonte, A.M. and Stafford, K.C. (2014) ‘The relationship between deer density, tick abundance, and human cases of Lyme disease in a residential community’, Journal of Medical Entomology, 51(4), pp. 777–784. https://doi.org/10.1603/ME13232
  75. Komar, N., Langevin, S., Hinten, S., et al. (2003) ‘Experimental infection of North American birds with the New York 1999 strain of West Nile virus’, Emerging Infectious Diseases, 9(3), pp. 311–322. https://doi.org/10.3201/eid0903.020628
  76. Kraemer, M.U.G., Reiner, R.C., Brady, O.J., et al. (2019) ‘Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus’, Nature Microbiology, 4(5), pp. 854–863. https://doi.org/10.1038/s41564-019-0376-y
  77. Kraemer, M.U.G., Sinka, M.E., Duda, K.A., et al. (2015) ‘The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus’, eLife, 4, e08347. https://doi.org/10.7554/eLife.08347
  78. Kraemer, M.U.G., Yang, C.-H., Gutierrez, B., et al. (2020) ‘The effect of human mobility and control measures on the COVID-19 epidemic in China’, Science, 368(6490), pp. 493–497. https://doi.org/10.1126/science.abb4218
  79. Levi, T., Keesing, F., Oggenfuss, K. et al. (2015) ‘Accelerated phenology of blacklegged ticks under climate warming’, Philosophical Transactions of the Royal Society B: Biological Sciences, 370(1665), 20130556. https://doi.org/10.1098/rstb.2013.0556
  80. Lindgren, F., Rue, H. and Lindström, J. (2011) ‘An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach’, Journal of the Royal Statistical Society: Series B, 73(4), pp. 423–498. https://doi.org/10.1111/j.1467-9868.2011.00777.x
  81. Linthicum, K.J., Anyamba, A., Tucker, C.J., et al. (1999) ‘Climate and satellite indicators to forecast Rift Valley fever epidemics in Kenya’, Science, 285(5426), pp. 397–400. https://doi.org/10.1126/science.285.5426.397
  82. LoGiudice, K., Ostfeld, R.S., Schmidt, K.A. et al. (2003) ‘The ecology of infectious disease: effects of host diversity and community composition on Lyme disease risk’, Proceedings of the National Academy of Sciences, 100(2), pp. 567–571. https://doi.org/10.1073/pnas.0233733100
  83. Lowe, R., Gasparrini, A., Van Meerbeeck, C.J., et al. (2018) ‘Nonlinear and delayed impacts of climate on dengue risk in Barbados: a modelling study’, PLoS Medicine, 15(7), e1002613. https://doi.org/10.1371/journal.pmed.1002613
  84. Lowe, R., Lee, S.A., O’Reilly, K.M., et al. (2021) ‘Combined effects of hydrometeorological hazards and urbanisation on dengue risk in Brazil: a spatiotemporal modelling study’, The Lancet Planetary Health, 5(4), e209–e219. https://doi.org/10.1016/S2542-5196(20)30292-8
  85. MacDonald, A.J. and Mordecai, E.A. (2019) ‘Amazon deforestation drives malaria transmission, and malaria burden reduces forest clearing’, Proceedings of the National Academy of Sciences, 116(44), pp. 22212–22218. https://doi.org/10.1073/pnas.1905315116
  86. MacKenzie, D.I., Nichols, J.D., Lachman, G.B., et al. (2002) ‘Estimating site occupancy rates when detection probabilities are less than one’, Ecology, 83(8), pp. 2248–2255. https://doi.org/10.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2
  87. Magori, K., Legros, M., Puente, M.E., et al. (2009) ‘Skeeter Buster: a stochastic, spatially explicit modeling tool for studying Aedes aegypti population replacement and population suppression strategies’, PLOS Neglected Tropical Diseases, 3(9), e508. https://doi.org/10.1371/journal.pntd.0000508
  88. Mahon, M.B., Sack, A., Aleuy, O.A., et al. (2024) ‘A meta-analysis of the biodiversity–disease relationship’, Nature, 629(8014), pp. 1073–1079. https://doi.org/10.1038/s41586-024-07380-6
  89. Martin, L.B., Burgan, S.C., Adelman, J.S. et al. (2016) ‘Host competence: an organismal trait to integrate immunology and epidemiology’, Integrative and Comparative Biology, 56(6), pp. 1225–1237. https://doi.org/10.1093/icb/icw064
  90. Mather, T.N., Wilson, M.L., Moore, S.I., et al. (1989) ‘Comparing the relative potential of rodents as reservoirs of the Lyme disease spirochete (Borrelia burgdorferi)’, American Journal of Epidemiology, 130(1), pp. 143–150. https://doi.org/10.1093/oxfordjournals.aje.a115306
  91. Messina, J.P., Brady, O.J., Golding, N., et al. (2019) ‘The current and future global distribution and population at risk of dengue’, Nature Microbiology, 4(9), pp. 1508–1515. https://doi.org/10.1038/s41564-019-0476-8
  92. Messina, J.P., Pigott, D.M., Golding, N., et al. (2015) ‘The global distribution of Crimean-Congo hemorrhagic fever’, Transactions of the Royal Society of Tropical Medicine and Hygiene, 109(8), pp. 503–513. https://doi.org/10.1093/trstmh/trv050
  93. Metcalf, C.J.E. and Lessler, J. (2017) ‘Opportunities and challenges in modeling emerging infectious diseases’, Science, 357(6347), pp. 149–152. https://doi.org/10.1126/science.aam8335
  94. Mordecai, E.A., Caldwell, J.M., Grossman, M.K., et al. (2019) ‘Thermal biology of mosquito-borne disease’, Ecology Letters, 22(10), pp. 1690–1708. https://doi.org/10.1111/ele.13335
  95. Mordecai, E.A., Cohen, J.M., Evans, M.V., et al. (2017) ‘Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models’, PLOS Neglected Tropical Diseases, 11(4), e0005568. https://doi.org/10.1371/journal.pntd.0005568
  96. Mordecai, E.A., Paaijmans, K.P., Johnson, L.R., et al. (2013) ‘Optimal temperature for malaria transmission is dramatically lower than previously predicted’, Ecology Letters, 16(1), pp. 22–30. https://doi.org/10.1111/ele.12015
  97. Murray, M.H., Sanchez, C.A., Becker, D.J., et al. (2019) ‘City sicker? A meta-analysis of wildlife health and urbanization’, Frontiers in Ecology and the Environment, 17(10), pp. 575–583. https://doi.org/10.1002/fee.2126
  98. Nguyen, T.-Q., Hutter, C.R., Markin, A., et al. (2025) ‘Emergence and interstate spread of highly pathogenic avian influenza A(H5N1) in dairy cattle in the United States’, Science, 388(6745), eadq0900. https://doi.org/10.1126/science.adq0900
  99. Nieto, N.C., Porter, W.T., Wachara, J.C., et al. (2018) ‘Using citizen science to describe the prevalence and distribution of tick bite and exposure to tick-borne diseases in the United States’, PLOS ONE, 13(7), e0199644. https://doi.org/10.1371/journal.pone.0199644
  100. Ogden, N.H. and Lindsay, L.R. (2016) ‘Effects of climate and climate change on vectors and vector-borne diseases: ticks are different’, Trends in Parasitology, 32(8), pp. 646–656. https://doi.org/10.1016/j.pt.2016.04.015
  101. Ogden, N.H., Bigras-Poulin, M., O’Callaghan, C.J., et al. (2005) ‘A dynamic population model to investigate effects of climate on geographic range and seasonality of the tick Ixodes scapularis’, International Journal for Parasitology, 35(4), pp. 375–389. https://doi.org/10.1016/j.ijpara.2004.12.013
  102. Olivero, J., Fa, J.E., Real, R., et al. (2017) ‘Recent loss of closed forests is associated with Ebola virus disease outbreaks’, Scientific Reports, 7(1), 14291. https://doi.org/10.1038/s41598-017-14727-9
  103. Oluwo, K., Bola-Sadipe, D. and Aliliele, C. (2025) ‘Conceptual framework for data analytics driven financial decision making in banking sector organizations’, Shodhshauryam, International Scientific Refereed Research Journal, 8(5), pp. 259–302. https://doi.org/10.32628/SHISRRJ258529
  104. Oluwo, K., Dada, T. and Isiekwu, C.P. (2024) ‘Transforming insurance underwriting with machine learning: a review and application cases’, International Journal of Advanced Multidisciplinary Research and Studies, 4(6), pp. 3008–3019. https://doi.org/10.62225/2583049x.2024.4.6.5876
  105. Ostfeld, R.S. and Keesing, F. (2000) ‘Biodiversity and disease risk: the case of Lyme disease’, Conservation Biology, 14(3), pp. 722–728. https://doi.org/10.1046/j.1523-1739.2000.99014.x
  106. Ostfeld, R.S. and Keesing, F. (2012) ‘Effects of host diversity on infectious disease’, Annual Review of Ecology, Evolution, and Systematics, 43(1), pp. 157–182. https://doi.org/10.1146/annurev-ecolsys-102710-145022
  107. Ovaskainen, O., Tikhonov, G., Norberg, A., et al. (2017) ‘How to make more out of community data? A conceptual framework and its implementation as models and software’, Ecology Letters, 20(5), pp. 561–576. https://doi.org/10.1111/ele.12757
  108. Parselia, E., Kontoes, C., Tsouni, A., et al. (2019) ‘Satellite Earth observation data in epidemiological modeling of malaria, dengue and West Nile virus: a scoping review’, Remote Sensing, 11(16), 1862. https://doi.org/10.3390/rs11161862
  109. Paull, S.H., Horton, D.E., Ashfaq, M., et al. (2017) ‘Drought and immunity determine the intensity of West Nile virus epidemics and climate change impacts’, Proceedings of the Royal Society B: Biological Sciences, 284(1848), 20162078. https://doi.org/10.1098/rspb.2016.2078
  110. Perkins, T.A., Scott, T.W., Le Menach, A., et al. (2013) ‘Heterogeneity, mixing, and the spatial scales of mosquito-borne pathogen transmission’, PLOS Computational Biology, 9(12), e1003327. https://doi.org/10.1371/journal.pcbi.1003327
  111. Pfenning-Butterworth, A., Buckley, L.B., Drake, J.M., et al. (2024) ‘Interconnecting global threats: climate change, biodiversity loss, and infectious diseases’, The Lancet Planetary Health, 8(4), e270–e283. https://doi.org/10.1016/S2542-5196(24)00021-4
  112. Phillips, S.J., Anderson, R.P. and Schapire, R.E. (2006) ‘Maximum entropy modeling of species geographic distributions’, Ecological Modelling, 190(3–4), pp. 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
  113. Randolph, S.E. and Rogers, D.J. (2000) ‘Fragile transmission cycles of tick-borne encephalitis virus may be disrupted by predicted climate change’, Proceedings of the Royal Society B: Biological Sciences, 267(1454), pp. 1741–1744. https://doi.org/10.1098/rspb.2000.1204
  114. Ranson, H. and Lissenden, N. (2016) ‘Insecticide resistance in African Anopheles mosquitoes: a worsening situation that needs urgent action to maintain malaria control’, Trends in Parasitology, 32(3), pp. 187–196. https://doi.org/10.1016/j.pt.2015.11.010
  115. Ray, E.L., Brooks, L.C., Bien, J., et al. (2023) ‘Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States’, International Journal of Forecasting, 39(3), pp. 1366–1383. https://doi.org/10.1016/j.ijforecast.2022.06.005
  116. Redding, D.W., Atkinson, P.M., Cunningham, A.A., et al. (2019) ‘Impacts of environmental and socio-economic factors on emergence and epidemic potential of Ebola in Africa’, Nature Communications, 10(1), 4531. https://doi.org/10.1038/s41467-019-12499-6
  117. Reiner, R.C., Perkins, T.A., Barker, C.M., et al. (2013) ‘A systematic review of mathematical models of mosquito-borne pathogen transmission: 1970–2010’, Journal of the Royal Society Interface, 10(81), 20120921. https://doi.org/10.1098/rsif.2012.0921
  118. Rohr, J.R., Barrett, C.B., Civitello, D.J., et al. (2019) ‘Emerging human infectious diseases and the links to global food production’, Nature Sustainability, 2(6), pp. 445–456. https://doi.org/10.1038/s41893-019-0293-3
  119. Rohr, J.R., Civitello, D.J., Halliday, F.W., et al. (2020) ‘Towards common ground in the biodiversity–disease debate’, Nature Ecology & Evolution, 4(1), pp. 24–33. https://doi.org/10.1038/s41559-019-1060-6
  120. Romanello, M., Walawender, M., Hsu, S.-C., et al. (2024) ‘The 2024 report of the Lancet Countdown on health and climate change: facing record-breaking threats from delayed action’, The Lancet, 404(10465), pp. 1847–1896. https://doi.org/10.1016/S0140-6736(24)01822-1
  121. RTS,S Clinical Trials Partnership (2015) ‘Efficacy and safety of RTS,S/AS01 malaria vaccine with or without a booster dose in infants and children in Africa: final results of a phase 3, individually randomised, controlled trial’, The Lancet, 386(9988), pp. 31–45. https://doi.org/10.1016/S0140-6736(15)60721-8
  122. Rue, H., Martino, S. and Chopin, N. (2009) ‘Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations’, Journal of the Royal Statistical Society: Series B, 71(2), pp. 319–392. https://doi.org/10.1111/j.1467-9868.2008.00700.x
  123. Rulli, M.C., D’Odorico, P., Galli, N., et al. (2025) ‘Land use change and infectious disease emergence’, Reviews of Geophysics, 63(2), e2022RG000785. https://doi.org/10.1029/2022RG000785
  124. Ryan, S.J., Carlson, C.J., Mordecai, E.A., et al. (2019) ‘Global expansion and redistribution of Aedes-borne virus transmission risk with climate change’, PLOS Neglected Tropical Diseases, 13(3), e0007213. https://doi.org/10.1371/journal.pntd.0007213
  125. Salkeld, D.J., Padgett, K.A. and Jones, J.H. (2013) ‘A meta-analysis suggesting that the relationship between biodiversity and risk of zoonotic pathogen transmission is idiosyncratic’, Ecology Letters, 16(5), pp. 679–686. https://doi.org/10.1111/ele.12101
  126. Shea, K., Runge, M.C., Pannell, D., et al. (2020) ‘Harnessing multiple models for outbreak management’, Science, 368(6491), pp. 577–579. https://doi.org/10.1126/science.abb9934
  127. Shocket, M.S., Verwillow, A.B., Numazu, M.G., et al. (2020) ‘Transmission of West Nile and five other temperate mosquito-borne viruses peaks at temperatures between 23°C and 26°C’, eLife, 9, e58511. https://doi.org/10.7554/eLife.58511
  128. Simpson, J.E., Hurtado, P.J., Medlock, J., et al. (2012) ‘Vector host-feeding preferences drive transmission of multi-host pathogens: West Nile virus as a model system’, Proceedings of the Royal Society B: Biological Sciences, 279(1730), pp. 925–933. https://doi.org/10.1098/rspb.2011.1282
  129. Smith, D.L. and McKenzie, F.E. (2004) ‘Statics and dynamics of malaria infection in Anopheles mosquitoes’, Malaria Journal, 3(1), 13. https://doi.org/10.1186/1475-2875-3-13
  130. Smith, D.L., Battle, K.E., Hay, S.I., et al. (2012) ‘Ross, Macdonald, and a theory for the dynamics and control of mosquito-transmitted pathogens’, PLoS Pathogens, 8(4), e1002588. https://doi.org/10.1371/journal.ppat.1002588
  131. Sokolow, S.H., Huttinger, E., Jouanard, N., et al. (2015) ‘Reduced transmission of human schistosomiasis after restoration of a native river prawn that preys on the snail intermediate host’, Proceedings of the National Academy of Sciences, 112(31), pp. 9650–9655. https://doi.org/10.1073/pnas.1502651112
  132. South, A. and Hastings, I.M. (2018) ‘Insecticide resistance evolution with mixtures and sequences: a model-based explanation’, Malaria Journal, 17(1), 80. https://doi.org/10.1186/s12936-018-2203-y
  133. Tizzoni, M., Bajardi, P., Decuyper, A., et al. (2014) ‘On the use of human mobility proxies for modeling epidemics’, PLOS Computational Biology, 10(7), e1003716. https://doi.org/10.1371/journal.pcbi.1003716
  134. Trebski, A., Gourlay, L., Gibb, R., et al. (2025) ‘Climate sensitivity is widely but unevenly spread across zoonotic diseases’, Proceedings of the National Academy of Sciences, 122(50), e2422851122. https://doi.org/10.1073/pnas.2422851122
  135. Tricou, V., Yu, D., Reynales, H., et al. (2024) ‘Long-term efficacy and safety of a tetravalent dengue vaccine (TAK-003): 4–5-year results from a phase 3, randomised, double-blind, placebo-controlled trial’, The Lancet Global Health, 12(2), pp. e257–e270. https://doi.org/10.1016/S2214-109X(23)00522-3
  136. Turner, H.C., Quyen, D.L., Dias, R., et al. (2023) ‘An economic evaluation of Wolbachia deployments for dengue control in Vietnam’, PLOS Neglected Tropical Diseases, 17(5), e0011356. https://doi.org/10.1371/journal.pntd.0011356
  137. Tusting, L.S., Willey, B., Lucas, H., et al. (2013) ‘Socioeconomic development as an intervention against malaria: a systematic review and meta-analysis’, The Lancet, 382(9896), pp. 963–972. https://doi.org/10.1016/S0140-6736(13)60851-X
  138. Utarini, A., Indriani, C., Ahmad, R.A., et al. (2021) ‘Efficacy of Wolbachia-infected mosquito deployments for the control of dengue’, New England Journal of Medicine, 384(23), pp. 2177–2186. https://doi.org/10.1056/NEJMoa2030243
  139. van Panhuis, W.G., Choisy, M., Xiong, X., et al. (2015) ‘Region-wide synchrony and traveling waves of dengue across eight countries in Southeast Asia’, Proceedings of the National Academy of Sciences, 112(42), pp. 13069–13074. https://doi.org/10.1073/pnas.1501375112
  140. Verelst, F., Willem, L. and Beutels, P. (2016) ‘Behavioural change models for infectious disease transmission: a systematic review (2010–2015)’, Journal of the Royal Society Interface, 13(125), 20160820. https://doi.org/10.1098/rsif.2016.0820
  141. Verhagen, J.H., Fouchier, R.A.M. and Lewis, N. (2021) ‘Highly pathogenic avian influenza viruses at the wild–domestic bird interface in Europe: future directions for research and surveillance’, Viruses, 13(2), 212. https://doi.org/10.3390/v13020212
  142. Voinov, A. and Bousquet, F. (2010) ‘Modelling with stakeholders’, Environmental Modelling & Software, 25(11), pp. 1268–1281. https://doi.org/10.1016/j.envsoft.2010.03.007
  143. Warton, D.I., Blanchet, F.G., O’Hara, R.B., et al. (2015) ‘So many variables: joint modeling in community ecology’, Trends in Ecology & Evolution, 30(12), pp. 766–779. https://doi.org/10.1016/j.tree.2015.09.007
  144. Wesolowski, A., Eagle, N., Tatem, A.J., et al. (2012) ‘Quantifying the impact of human mobility on malaria’, Science, 338(6104), pp. 267–270. https://doi.org/10.1126/science.1223467
  145. Whitmee, S., Haines, A., Beyer, C., et al. (2015) ‘Safeguarding human health in the Anthropocene epoch: report of The Rockefeller Foundation–Lancet Commission on planetary health’, The Lancet, 386(10007), pp. 1973–2028. https://doi.org/10.1016/S0140-6736(15)60901-1
  146. Zinsstag, J., Schelling, E., Waltner-Toews, D., et al. (2011) ‘From “one medicine” to “one health” and systemic approaches to health and well-being’, Preventive Veterinary Medicine, 101(3–4), pp. 148–156. https://doi.org/10.1016/j.prevetmed.2010.07.003

Spotlight Articles

Article 6 of the Paris Agreement: A Comprehensive Review of Mechanisms, Progress, and Persistent Challenges

Drug and Device World, INDIA.

Effect of Losartan in Reducing Microalbuminuria on Diabetic Patient

Rajshahi Medical College and University of Rajshahi, BANGLADESH.

Uncertainty Estimation in Predicting Oxygenation by Plunging Jet Aerators Using Probabilistic Machine Learning and Conformal Prediction

National Institute of Technology Kurukshetra, Kurukshetra, INDIA.

A Futuristic Perspective of the Usage of AI: Growth, Merits and Limitations

Royal Melbourne Institute of Technology (RMIT), Melbourne, AUSTRALIA.

Technical Design and Sustainability Analysis of Rooftop On-Grid Photovoltaic System for Communal Religious Buildings

Widya Mandala Catholic University Surabaya, INDONESIA.

A Critical Growth Analysis of Industrial and Professional Services Robots Installed and in Operation Worldwide

Wychar Labs, Dallas, Texas, UNITED STATES OF AMERICA (USA). 

Impact of Balanced Use of Fertilizer on Crop Production and Its Quality for Human Health

Agri. Services, Islamabad Model College for Girls, and Riphah International University, PAKISTAN.

Modeling Groundwater Velocity Response to Permeability and Storage Coefficient Variations in Confined Gravel Aquifers of Nigeria

Rivers State University, NIGERIA.

Sustainable Development and the Role of the Indian Judiciary in Promoting It with Special Reference to the Precautionary Principle and the Polluter Pays Principle

Durham University, Durham, UNITED KINGDOM (UK).

Analyzing the Spread and Impact of 5G-Corona Misinformation on Twitter: A Statistical Approach

Kampala International University, UGANDA; Rivers State University, NIGERIA.

Latest Articles

See more from the latest issue
Archives