Search Advanced

AIMS Public Health

2020, Volume 7, Issue 4: 854-868. doi: 10.3934/publichealth.2020066
Previous Article Next Article
Research article Special Issues

Association of climatic factors with COVID-19 in Pakistan

  • Canadian Academy of Osteopathy, 66 Ottawa Street North, Canada
  • Received: 02 September 2020 Accepted: 06 November 2020 Published: 11 November 2020

  • Introduction Environmental factors such as wind, temperature, humidity, and sun exposure are known to affect influenza and viruses such as severe acute respiratory syndrome (SARS) and Middle East Respiratory Syndrome (MERS) transmissions. COVID-19 is a new pandemic with very little information available about its transmission and association with environmental factors. The goal of this paper is to explore the association of environmental factors on daily incidence rate, mortality rate, and recoveries of COVID-19.
    Methods The environmental data for humidity, temperature, wind, and sun exposure were recorded from metrological websites and COVID-19 data such as the daily incidence rate, death rate, and daily recovery were extracted from the government's official website available to the general public. The analysis for each outcome was adjusted for factors such as lock down status, nationwide events, and the number of daily tests performed. Analysis was completed with negative binominal regression log link using generalised linear modelling.
    Results Daily temperature, sun exposure, wind, and humidity were not significantly associated with daily incidence rate. Temperature and nationwide social gatherings, although non-significant, showed trends towards a higher chance of incidence. An increase in the number of daily testing was significantly associated with higher COVID-19 incidences (effect size ranged from 2.17–9.96). No factors were significantly associated with daily death rates. Except for the province of Balochistan, a lower daily temperature was associated with a significantly higher daily recovery rate.
    Discussion Environmental factors such as temperature, humidity, wind, and daily sun exposure were not consistently associated with COVID-19 incidence, death rates, or recovery. More policing about precautionary measures and ensuring diagnostic testing and accuracy are needed.

    Citation: Yasir Rehman, Nadia Rehman. Association of climatic factors with COVID-19 in Pakistan[J]. AIMS Public Health, 2020, 7(4): 854-868. doi: 10.3934/publichealth.2020066

    Related Papers:

    [1] Christopher M. Kribs-Zaleta . Sociological phenomena as multiple nonlinearities: MTBI's new metaphor for complex human interactions. Mathematical Biosciences and Engineering, 2013, 10(5&6): 1587-1607. doi: 10.3934/mbe.2013.10.1587
    [2] Abba B. Gumel, Baojun Song . Existence of multiple-stable equilibria for a multi-drug-resistant model of mycobacterium tuberculosis. Mathematical Biosciences and Engineering, 2008, 5(3): 437-455. doi: 10.3934/mbe.2008.5.437
    [3] Yuyi Xue, Yanni Xiao . Analysis of a multiscale HIV-1 model coupling within-host viral dynamics and between-host transmission dynamics. Mathematical Biosciences and Engineering, 2020, 17(6): 6720-6736. doi: 10.3934/mbe.2020350
    [4] Rachel Clipp, Brooke Steele . An evaluation of dynamic outlet boundary conditions in a 1D fluid dynamics model. Mathematical Biosciences and Engineering, 2012, 9(1): 61-74. doi: 10.3934/mbe.2012.9.61
    [5] Bertin Hoffmann, Udo Schumacher, Gero Wedemann . Absence of convection in solid tumors caused by raised interstitial fluid pressure severely limits success of chemotherapy—a numerical study in cancers. Mathematical Biosciences and Engineering, 2020, 17(5): 6128-6148. doi: 10.3934/mbe.2020325
    [6] John Boscoh H. Njagarah, Farai Nyabadza . Modelling the role of drug barons on the prevalence of drug epidemics. Mathematical Biosciences and Engineering, 2013, 10(3): 843-860. doi: 10.3934/mbe.2013.10.843
    [7] Yubo Sun, Yuanyuan Cheng, Yugen You, Yue Wang, Zhizhong Zhu, Yang Yu, Jianda Han, Jialing Wu, Ningbo Yu . A novel plantar pressure analysis method to signify gait dynamics in Parkinson's disease. Mathematical Biosciences and Engineering, 2023, 20(8): 13474-13490. doi: 10.3934/mbe.2023601
    [8] Mingzhu Qu, Chunrui Zhang, Xingjian Wang . Analysis of dynamic properties on forest restoration-population pressure model. Mathematical Biosciences and Engineering, 2020, 17(4): 3567-3581. doi: 10.3934/mbe.2020201
    [9] Salih Djillali, Soufiane Bentout, Tarik Mohammed Touaoula, Abdessamad Tridane . Global dynamics of alcoholism epidemic model with distributed delays. Mathematical Biosciences and Engineering, 2021, 18(6): 8245-8256. doi: 10.3934/mbe.2021409
    [10] Tianqi Song, Chuncheng Wang, Boping Tian . Mathematical models for within-host competition of malaria parasites. Mathematical Biosciences and Engineering, 2019, 16(6): 6623-6653. doi: 10.3934/mbe.2019330
  • Introduction Environmental factors such as wind, temperature, humidity, and sun exposure are known to affect influenza and viruses such as severe acute respiratory syndrome (SARS) and Middle East Respiratory Syndrome (MERS) transmissions. COVID-19 is a new pandemic with very little information available about its transmission and association with environmental factors. The goal of this paper is to explore the association of environmental factors on daily incidence rate, mortality rate, and recoveries of COVID-19.
    Methods The environmental data for humidity, temperature, wind, and sun exposure were recorded from metrological websites and COVID-19 data such as the daily incidence rate, death rate, and daily recovery were extracted from the government's official website available to the general public. The analysis for each outcome was adjusted for factors such as lock down status, nationwide events, and the number of daily tests performed. Analysis was completed with negative binominal regression log link using generalised linear modelling.
    Results Daily temperature, sun exposure, wind, and humidity were not significantly associated with daily incidence rate. Temperature and nationwide social gatherings, although non-significant, showed trends towards a higher chance of incidence. An increase in the number of daily testing was significantly associated with higher COVID-19 incidences (effect size ranged from 2.17–9.96). No factors were significantly associated with daily death rates. Except for the province of Balochistan, a lower daily temperature was associated with a significantly higher daily recovery rate.
    Discussion Environmental factors such as temperature, humidity, wind, and daily sun exposure were not consistently associated with COVID-19 incidence, death rates, or recovery. More policing about precautionary measures and ensuring diagnostic testing and accuracy are needed.




    Conflict of interest


    All authors declare no conflicts of interest in this paper.

    [1] Zhu N, Zhang D, Wang W, et al. (2020) A Novel Coronavirus from Patients with Pneumonia in China, 2019. N Engl J Med 382: 727-733.
    [2] Sahin AREA, Agaoglu PM, Dineri Y, et al. (2020) 2019 Novel Coronavirus (COVID-19) Outbreak: A Review of the Current Literature. EJMO 4: 1-7.
    [3] Wang L, Wang Y, Ye D, et al. (2020) Review of the 2019 novel coronavirus (SARS-CoV-2) based on current evidence. Int J Antimicrob Agents 55: 105948-105948.
    [4] WHO COVID-19 Public Health Emergency of International Concern (PHEIC). Global research and innovation forum: towards a research roadmap (2020) .Available from: https://www.who.int/publications/m/item/covid-19-public-health-emergency-of-international-concern-(pheic)-global-research-and-innovation-forum.
    [5] Lai CC, Wang CY, Wang YH, et al. (2020) Global epidemiology of coronavirus disease 2019 (COVID-19): disease incidence, daily cumulative index, mortality, and their association with country healthcare resources and economic status. Int J Antimicrob Agents 55: 105946.
    [6] Clark A, Jit M, Warren-Gash C, et al. (2020) Global, regional, and national estimates of the population at increased risk of severe COVID-19 due to underlying health conditions in 2020: a modelling study. Lancet Global Health 8: e1003-e1017.
    [7] Bilgin S, Kurtkulagi O, Bakir Kahveci G, et al. (2020) Millennium pandemic: A review of coronavirus disease (COVID-19). Exp Biomed Res 3: 117-125.
    [8] Waris A, Atta UK, Ali M, et al. (2020) COVID-19 outbreak: current scenario of Pakistan. New Microbes New Infect 35: 100681.
    [9] Bashir MF, Ma BJ, Bilal, et al. (2020) Correlation between environmental pollution indicators and COVID-19 pandemic: A brief study in Californian context. Environ Res 187: 109652-109652.
    [10] DAWN PM Imran hopeful Pakistan's ‘hot and dry’ weather will mitigate virus threat Dawn News TV (2020) .Available from: https://www.dawn.com/news/1542413.
    [11] Marr LC, Tang JW, Van Mullekom J, et al. (2019) Mechanistic insights into the effect of humidity on airborne influenza virus survival, transmission and incidence. J R Soc Interface 16: 20180298.
    [12] Park JE, Son WS, Ryu Y, et al. (2020) Effects of temperature, humidity, and diurnal temperature range on influenza incidence in a temperate region. Influenza Other Respir Viruses 14: 11-18.
    [13] Ellwanger JH, Chies JAB (2018) Wind: a neglected factor in the spread of infectious diseases. Lancet Planet Health 2.
    [14] Hobday RA, Dancer SJ (2013) Roles of sunlight and natural ventilation for controlling infection: historical and current perspectives. J Hosp Infect 84: 271-282.
    [15] Altamimi A, Ahmed AE (2020) Climate factors and incidence of Middle East respiratory syndrome coronavirus. J Infect Public Health 13: 704-708.
    [16] Gardner EG, Kelton D, Poljak Z, et al. (2019) A case-crossover analysis of the impact of weather on primary cases of Middle East respiratory syndrome. BMC Infect Dis 19: 113.
    [17] van Doremalen N, Bushmaker T, Munster VJ (2013) Stability of Middle East respiratory syndrome coronavirus (MERS-CoV) under different environmental conditions. Eurosurveillance 18: 20590.
    [18] Darnell MER, Subbarao K, Feinstone SM, et al. (2004) Inactivation of the coronavirus that induces severe acute respiratory syndrome, SARS-CoV. J Virol Methods 121: 85-91.
    [19] Cai QC, Lu J, Xu QF, et al. (2007) Influence of meteorological factors and air pollution on the outbreak of severe acute respiratory syndrome. Public Health 121: 258-265.
    [20] Bi P, Wang J, Hiller JE (2007) Weather: driving force behind the transmission of severe acute respiratory syndrome in China? Intern Med J 37: 550-554.
    [21] Casanova LM, Jeon S, Rutala WA, et al. (2010) Effects of Air Temperature and Relative Humidity on Coronavirus Survival on Surfaces. Appl Environ Microbiol 76: 2712-2717.
    [22] Khan MD, Thi Vu HH, Lai QT, et al. (2019) Aggravation of Human Diseases and Climate Change Nexus. Int J Environ Res Public Health 16: 2799.
    [23] Faust JS, del Rio C (2020) Assessment of Deaths From COVID-19 and From Seasonal Influenza. JAMA Intern Med 180: 1045-1046.
    [24] Centers for Disease Control and Prevention Similarities and Differences between Flu and COVID-19. Centers for Disease Control and Prevention, National Center for Immunization and Respiratory Diseases (NCIRD) (2020) .Available from: https://www.cdc.gov/flu/symptoms/flu-vs-covid19.htm.
    [25] Adekunle IA, Tella SA, Oyesiku KO, et al. (2020) Spatio-temporal analysis of meteorological factors in abating the spread of COVID-19 in Africa. Heliyon 6: e04749.
    [26] Parodi SM, Liu VX (2020) From Containment to Mitigation of COVID-19 in the US. JAMA 323: 1441-1442.
    [27] Ma Y, Zhao Y, Liu J, et al. (2020) Effects of temperature variation and humidity on the death of COVID-19 in Wuhan, China. Sci Total Environ 724: 138226-138226.
    [28] Luo W, Majumder MS, Liu D, et al. The role of absolute humidity on transmission rates of the COVID-19 outbreak. medRxiv (2020) .2020:2020.2002.2012.20022467.
    [29] Government of Pakistan Pakistan Cases Details (2020) .Available from: http://covid.gov.pk/stats/pakistan.
    [30] Worldometer Coronavirus Cases: Pakistan (2020) .Available from: https://www.worldometers.info/coronavirus/country/pakistan/.
    [31] Rosario DKA, Mutz YS, Bernardes PC, et al. (2020) Relationship between COVID-19 and weather: Case study in a tropical country. Int J Hyg Environ Health 229: 113587-113587.
    [32] Pakistan Meteorological Department, Government of Pakistan Pakistan Meteorological Department (2020) .Available from: http://www.pmd.gov.pk/en/.
    [33] Time and Data Weather in Pakistan (2020) .Available from: https://www.timeanddate.com/weather/pakistan.
    [34] Weather Pakistan Weather (2020) .Available from: https://www.accuweather.com.
    [35] Yun J, Greiner M, Holler C, et al. (2016) Association between the ambient temperature and the occurrence of human Salmonella and Campylobacter infections. Sci Rep 6: 28442.
    [36] Hernandez E, Torres R, Joyce AL (2019) Environmental and Sociological Factors Associated with the Incidence of West Nile Virus Cases in the Northern San Joaquin Valley of California, 2011–2015. Vector Borne Zoonotic Dis 19: 851-858.
    [37] Crainiceanu CM, Ruppert D (2004) Likelihood ratio tests for goodness-of-fit of a nonlinear regression model. J Multivar Anal 91: 35-52.
    [38] Qi H, Xiao S, Shi R, et al. (2020) COVID-19 transmission in Mainland China is associated with temperature and humidity: A time-series analysis. Sci Total Environ 728: 138778-138778.
    [39] Sarmadi M, Marufi N, Kazemi Moghaddam V (2020) Association of COVID-19 global distribution and environmental and demographic factors: An updated three-month study. Environ Res 188: 109748-109748.
    [40] Şahin M (2020) Impact of weather on COVID-19 pandemic in Turkey. Sci Total Environ 728: 138810.
    [41] Iqbal MM, Abid I, Hussain S, et al. (2020) The effects of regional climatic condition on the spread of COVID-19 at global scale. Sci Total Environ 739: 140101-140101.
    [42] Tanni SE, Patino CM, Ferreira JC (2020) Correlation vs. regression in association studies. Jornal Brasileiro de Pneumologia 46.
    [43] Zhu Y, Xie J (2020) Association between ambient temperature and COVID-19 infection in 122 cities from China. Sci Total Environ 724: 138201.
    [44] BBC New Zealand lifts all Covid restrictions, declaring the nation virus-free (2020) .Available from: https://www.bbc.com/news/world-asia-52961539.
    [45] Weather2Visit Auckland Weather in June, What's the weather like in Auckland, New Zealand in June 2020? Available from: https://www.weather2visit.com/australia-pacific/new-zealand/auckland-june.htm.
    [46] Lewis D We felt we had beaten it': New Zealand's race to eliminate the coronavirus again. Nature (2020) .Available from: https://www.nature.com/articles/d41586-020-02402-5.
    [47] Perry N After 102 days coronavirus-free, New Zealand reports 4 new cases. Global News (2020) .Available from: https://www.nature.com/articles/d41586-020-02402-5.
    [48] India TTo Coronavirus: India crosses 80,000 cases in a day, first country to do so. The Times of India (2020) .Available from: https://timesofindia.indiatimes.com/india/india-crosses-80000-cases-in-a-day-first-country-to-do-so/articleshow/77841508.cms.
    [49] BBC News Coronavirus: India surpasses US for highest single-day rise in Covid-19 cases (2020) .Available from: https://www.bbc.com/urdu/regional-53948433.
    [50] Chin AWH, Chu JTS, Perera MRA, et al. (2020) Stability of SARS-CoV-2 in different environmental conditions. Lancet Microbe 1.
    [51] The New York Times Imams Overrule Pakistan's Coronavirus Lockdown as Ramadan Nears (2020) .Available from: https://www.nytimes.com/2020/04/23/world/asia/pakistan-coronavirus-ramadan.html.
    [52] Farmer B Pakistan will reimpose lockdown if residents don't observe safety rules, ministers warn. The National AE (2020) .Available from: https://www.thenational.ae/world/asia/pakistan-will-reimpose-lockdown-if-residents-don-t-observe-safety-rules-ministers-warn-1.1018961.
    [53] Whiting K Two experts explain what other viruses can teach us about COVID-19—and what they can't. The World Economic Forum COVID Action Platform (2020) .Available from: https://www.weforum.org/agenda/2020/03/coronavirus-covid-19-mers-sars-experts/.
    [54] Jonathan Lambert Warm weather probably won't slow COVID-19 transmission much. Any seasonal benefit can be canceled by humanity's vulnerability to the virus, a study suggests. Science News (2020) .Available from: https://www.sciencenews.org/article/coronavirus-warm-weather-will-not-slow-covid-19-transmission.

  • This article has been cited by:

    1. Diana M. Thomas, Marion Weedermann, Bernard F. Fuemmeler, Corby K. Martin, Nikhil V. Dhurandhar, Carl Bredlau, Steven B. Heymsfield, Eric Ravussin, Claude Bouchard, Dynamic model predicting overweight, obesity, and extreme obesity prevalence trends, 2014, 22, 19307381, 590, 10.1002/oby.20520
    2. Paul J. Gruenewald, William R. Ponicki, Lillian G. Remer, Fred W. Johnson, Lance A. Waller, Dennis M. Gorman, Li Zhu, 2013, Chapter 9, 978-90-481-8920-5, 167, 10.1007/978-90-481-8921-2_9
    3. D. M. G. Comissiong, J. Sooknanan, A review of the use of optimal control in social models, 2018, 6, 2195-268X, 1841, 10.1007/s40435-018-0405-3
    4. Paul J. Gruenewald, William R. Ponicki, Lillian G. Remer, Lance A. Waller, Li Zhu, Dennis M. Gorman, Mapping the Spread of Methamphetamine Abuse in California From 1995 to 2008, 2013, 103, 0090-0036, 1262, 10.2105/AJPH.2012.300779
    5. L.H.A. Monteiro, Population dynamics in educational institutions considering the student satisfaction, 2016, 30, 10075704, 236, 10.1016/j.cnsns.2015.06.015
    6. Fabio Sánchez, Xiaohong Wang, Carlos Castillo-Chávez, Dennis M. Gorman, Paul J. Gruenewald, 2007, 9780123694294, 353, 10.1016/B978-012369429-4/50046-X
    7. Baojun Song, Zhilan Feng, Gerardo Chowell, From the guest editors, 2013, 10, 1551-0018, 10.3934/mbe.2013.10.5i
    8. Fabio Sanchez, Juan G. Calvo, Esteban Segura, Zhilan Feng, A partial differential equation model with age-structure and nonlinear recidivism: Conditions for a backward bifurcation and a general numerical implementation, 2019, 78, 08981221, 3916, 10.1016/j.camwa.2019.06.021
    9. David Greenhalgh, Martin Griffiths, Dynamic phenomena arising from an extended Core Group model, 2009, 221, 00255564, 136, 10.1016/j.mbs.2009.08.003
    10. Daniel M. Romero, Christopher M. Kribs-Zaleta, Anuj Mubayi, Clara Orbe, An epidemiological approach to the spread of political third parties, 2011, 15, 1553-524X, 707, 10.3934/dcdsb.2011.15.707
    11. Francisco-José Santonja, Iván-C. Lombana, María Rubio, Emilio Sánchez, Javier Villanueva, A network model for the short-term prediction of the evolution of cocaine consumption in Spain, 2010, 52, 08957177, 1023, 10.1016/j.mcm.2010.02.032
    12. Aqsa Nazir, Naveed Ahmed, Umar Khan, Syed Tauseef Mohyud-Din, A conformable mathematical model for alcohol consumption in Spain, 2019, 12, 1793-5245, 1950057, 10.1142/S1793524519500578
    13. Anuj Mubayi, Priscilla E. Greenwood, Carlos Castillo-Chávez, Paul J. Gruenewald, Dennis M. Gorman, The impact of relative residence times on the distribution of heavy drinkers in highly distinct environments, 2010, 44, 00380121, 45, 10.1016/j.seps.2009.02.002
    14. Bechir Amdouni, Marlio Paredes, Christopher Kribs, Anuj Mubayi, Why do students quit school? Implications from a dynamical modelling study, 2017, 473, 1364-5021, 20160204, 10.1098/rspa.2016.0204
    15. Yun Kang, Carlos Castillo-Chávez, A simple epidemiological model for populations in the wild with Allee effects and disease-modified fitness, 2014, 19, 1553-524X, 89, 10.3934/dcdsb.2014.19.89
    16. Stephen Wirkus, Christopher Kribs-Zaleta, Erika T. Camacho, The mathematical and theoretical biology institute - a model of mentorship through research, 2013, 10, 1551-0018, 1351, 10.3934/mbe.2013.10.1351
    17. Francisco-José Santonja, Emilio Sánchez, María Rubio, José-Luis Morera, Alcohol consumption in Spain and its economic cost: A mathematical modeling approach, 2010, 52, 08957177, 999, 10.1016/j.mcm.2010.02.029
    18. Emilio Sánchez, Rafael-Jacinto Villanueva, Francisco-José Santonja, María Rubio, Predicting cocaine consumption in Spain: A mathematical modelling approach, 2011, 18, 0968-7637, 108, 10.3109/09687630903443299
    19. Daniel M. Romero, Christopher M. Kribs-Zaleta, Anuj Mubayi, Clara Orbe, An Epidemiological Approach to the Spread of Political Third Parties, 2009, 1556-5068, 10.2139/ssrn.1503124
    20. Sunhwa Choi, Jonggul Lee, Eunok Jung, OPTIMAL STRATEGIES FOR PREVENTION OF ECSTASY USE, 2014, 18, 1226-9433, 1, 10.12941/jksiam.2014.18.001
    21. J. Sooknanan, D.M.G. Comissiong, A mathematical model for the treatment of delinquent behaviour, 2018, 63, 00380121, 60, 10.1016/j.seps.2017.08.001
    22. Joanna Sooknanan, Donna M. G. Comissiong, When behaviour turns contagious: the use of deterministic epidemiological models in modeling social contagion phenomena, 2017, 5, 2195-268X, 1046, 10.1007/s40435-016-0271-9
    23. Christopher Kribs-Zaleta, Sociological phenomena as multiple nonlinearities: MTBI's new metaphor for complex human interactions, 2013, 10, 1551-0018, 1587, 10.3934/mbe.2013.10.1587
    24. Ariel Cintrón-Arias, Fabio Sánchez, Xiaohong Wang, Carlos Castillo-Chavez, Dennis M. Gorman, Paul J. Gruenewald, 2009, Chapter 14, 978-90-481-2312-4, 343, 10.1007/978-90-481-2313-1_14
    25. Caroline E. Walters, Jeremy R. Kendal, An SIS model for cultural trait transmission with conformity bias, 2013, 90, 00405809, 56, 10.1016/j.tpb.2013.09.010
    26. Yusra Bibi Ruhomally, Muhammad Zaid Dauhoo, Laurent Dumas, A graph cellular automaton with relation-based neighbourhood describing the impact of peer influence on the consumption of marijuana among college-aged youths, 2021, 0, 2164-6074, 0, 10.3934/jdg.2021011
    27. Theophilus Kwofie, Matthias Dogbatsey, Stephen E. Moore, Curtailing crime dynamics: A mathematical approach, 2023, 8, 2297-4687, 10.3389/fams.2022.1086745
    28. Baojun Song, Basic reinfection number and backward bifurcation, 2021, 18, 1551-0018, 8064, 10.3934/mbe.2021400
    29. Cesar Montalvo-Clavijo, Carlos Castillo-Chavez, Charles Perrings, Anuj Mubayi, Neighborhood effects, college education, and social mobility, 2022, 00380121, 101471, 10.1016/j.seps.2022.101471
    30. Christopher Anaya, Clara Burgos, Juan-Carlos Cortés, Rafael-J. Villanueva, Capturing the Data Uncertainty Change in the Cocaine Consumption in Spain Using an Epidemiologically Based Model, 2016, 2016, 1085-3375, 1, 10.1155/2016/1758459
    31. Julia Calatayud, Marc Jornet, Jorge Mateu, Spatial modeling of crime dynamics: Patch and reaction–diffusion compartmental systems, 2023, 0170-4214, 10.1002/mma.9064
    32. Fabio Sanchez, Jorge Arroyo-Esquivel, Juan G. Calvo, A mathematical model with nonlinear relapse: conditions for a forward-backward bifurcation, 2023, 17, 1751-3758, 10.1080/17513758.2023.2192238
    33. Yusra Bibi Ruhomally, Muhammad Zaid Dauhoo, 2023, 9789815079241, 15, 10.2174/9789815079241123010005
    34. Julia Calatayud, Marc Jornet, Jorge Mateu, A dynamical mathematical model for crime evolution based on a compartmental system with interactions, 2024, 0020-7160, 1, 10.1080/00207160.2024.2302840
    35. Leigh B. Pearcy, Suzanne Lenhart, W. Christopher Strickland, Structural instability and linear allocation control in generalized models of substance use disorder, 2024, 371, 00255564, 109169, 10.1016/j.mbs.2024.109169
  • Reader Comments
  • © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索
AIMS Public Health
3.1 4.8

Metrics

Article views(4559) PDF downloads(134) Cited by(8)

Preview PDF
Download XML
Export Citation
Article outline
Abstract
References

Other Articles By Authors

Related pages

Tools

Copyright © AIMS Press

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog