RESEARCH SPECIALIZATION
Medical Geography / Spatial Epidemiology
GIS & Remote Sensing in Public Health
Spatial Statistics and Modeling
Systematic Review and Meta-Analysis
Applied Machine Learning in Public Health
Embedded Files
CURRENT FUNDING
NASA
Funding Period: 1/1/2025–30/12/2027
Role: Co-Investigator
Project Title: Forecasting Mosquito-Borne Disease Risk in a Changing Climate: Integrating GLOBE Citizen Science and NASA Earth System Modeling
NIH (CTSA)
Funding Period: 4/1/2025–03/31/2032
Role: Co-Investigator
Project Title: South Carolina Clinical & Translational Research Institute (SCTR)
GRANTS (UNDER REVIEW)
NIH/NIA (K01)
Role: Principal Investigator
Project Title: Bayesian Mediation Analysis of Neighborhood Effects on Rural-Urban Disparities in Alzheimer’s Disease Dementia
NIH/NCI ITCR (U01)
Role: Co-Investigator
Project Title: Evaluation and Usability Enhancement of All of Us Workbench for Cancer Research
NIH (R01)
Role: Co-Investigator
Project Title: Geographic Areas and Prevention of Type 2 Diabetes
NIH (R01)
Role: Co-Investigator
Project Title: Adverse Glycemic Episodes in Hospitalized Patients with Type 2 Diabetes: Predictors, Outcomes, and Post-Hospital Care
NIH (R01)
Role: Co-Investigator
Project Title: Early-onset Heart Disease across Rural and Urban Areas in the US Midwest and South
Gates Foundation
Role: Co-Investigator
Project Title: Modeling Maternal Multimorbidity in Sub-Saharan Africa: Project MoMM
NIH/NCI (U54) [Unfunded]
Role: Co-Investigator
Project Title: Utilizing geospatial modeling to characterize environmental and social determinants on Triple Negative Breast Cancer outcomes in South Carolina.
PEER-REVIEWED PUBLICATIONS ( *Corresponding Author)
Total Citation Count: 2550+ H-Index: 25 i10-Index: 30
49 – Kianfar N., Alsharayri S., Mollalo A.* Alzheimer’s Disease and Related Dementias in Rural U.S. Medicare Populations: A Scoping Review (Under Review)
48 – Kianfar N.*, Yao X.A., Kiani B., Mollalo A. Health Disparity in Spatial Accessibility to Emergency Cardiac Care Centers A Case Study in Georgia, USA (Under Review)
47 – Aschenbrenner O., Hashemi Tonekaboni N., Kramer M., Mollalo A.* Long-term Exposure to Air and Alzheimer’s Disease Dementia Prevalence Across the Contiguous United States: An Explainable Machine Learning Analysis. IEEE-EMBS International Conference on Biomedical and Health Informatics (Accepted)
46 – Kramer M., Alekseyenko A.V., Mollalo A.* (2025) The Role of Built and Social Environments in Alzheimer’s Disease Dementia Prevalence in the United States: A Machine Learning Approach. Journal of Alzheimer's Disease (https://doi.org/10.1177/13872877251372144)
45 – Mollalo A.*, Kramer M., Cutty M., Hoseini B. (2025) A Systematic Review and Meta-Analysis of Rural-Urban Disparities in Alzheimer’s Disease Dementia Prevalence. The Journal of Prevention of Alzheimer's Disease (https://doi.org/10.1016/j.tjpad.2025.100305)
44 – Shao W., Mollalo A., Hashemi Tonekaboni N.* (2025) Spatio-temporal analysis of foot traffic dynamics in Charleston County, South Carolina: Before, during, and after COVID-19. Geospatial Health (https://doi.org/10.4081/gh.2025.1363)
43 – Mollalo A.*, Grekousis G., Florez H., Neelon B., Lenert L.A., Alekseyenko A.V. (2025) Alzheimer's Disease Dementia Prevalence in the United States: A County-Level Spatial Machine Learning Analysis. American Journal of Alzheimer's Disease and other Dementias (https://doi.org/10.1177/15333175251335570)
42 – Kramer M., Cutty M., Knox S., Alekseyenko A. V., Mollalo A.* (2025) Rural-urban disparities of Alzheimer's disease and related dementias: A scoping review. Alzheimer's & Dementia: Translational Research & Clinical Interventions (https://doi.org/10.1002/trc2.70047)
41 – Baminiwatte R., Torsu B., Scherbakov D., Mollalo A., Obeid J., Alekseyenko A.V., Lenert L.A.* (2024) Machine Learning in Healthcare Citizen Science: A Scoping Review. International Journal of Medical Informatics 105766 (https://doi.org/10.1016/j.ijmedinf.2024.105766).
40 – Mollalo A.*, Knox S., Meng J., Benitez A., Lenert L.A., Alekseyenko A.V. (2024) Geospatial Analysis of the Association between Medicaid Expansion, Minimum Wage Policies, and Alzheimer's Disease Dementia Prevalence in the United States. Information 15 (11) (https://doi.org/10.3390/info15110688)
39 – Mollalo A.*, Hamidi B., Lenert L.A., Alekseyenko A.V. (2024) Application of Spatial Analysis on Electronic Health Records to Characterize Patient Phenotypes: Systematic Review. JMIR Medical Informatics 12:e56343 (https://doi.org/10.2196/56343)
38 – Ebrahimi A.H., Alesheikh A.A. *, Hooshangi N., Sharif M., Mollalo A. (2024) Modeling COVID-19 Transmission in Closed Indoor Settings: An Agent-based Approach with Comprehensive Sensitivity Analysis. Information 15(6) 362 (https://doi.org/10.3390/info15060362)
37 –Luke R.A., Shaw Jr G., Clarke G.S., Mollalo A.* (2024) Identifying Long COVID Definitions, Predictors, and Risk Factors using Electronic Health Records: A Scoping Review, Informatics 11(2) 41 (https://doi.org/10.3390/informatics11020041)
36 – Scherbakov D., Mollalo A., Lenert L.*. (2024) Stressful life events in electronic health records: a scoping review, Journal of the American Medical Informatics Association 31(4) 1025-35 (https://doi.org/10.1093/jamia/ocae023)
35 – Tabasi M., Alesheikh A.A., Kalantari M., Mollalo A., Hatamiafkoueieh J.* (2023) Spatio-Temporal Modeling of COVID-19 Spread in Relation to Urban Land Uses: An Agent-Based Approach. Sustainability 15(18), 13827 (https://doi.org/10.3390/su151813827)
34 – Hasanpour A.H., Sepidarkish M., Mollalo A., Ardekani A., Almukhtar M., Mechaal A., Hosseini S.R., Bayani M., Javanian M., Rostami A.* (2023) The global prevalence of methicillin-resistant Staphylococcus aureus colonization in residents of elderly care centers: a systematic review and meta-analysis. Antimicrobial Resistance and Infection Control (https://doi.org/10.1186/s13756-023-01210-6)
33 – Naeimi R., Sepidarkish M., Mollalo A., Parsa H., Mahjour S., Safarpour F., Almukhtar M., Mechaal A., Chemaitelly H., Sartip B., Marhoommirzabak E., Ardekani A., Hotez P.J., Gasser R.B.*, Rostami A.* (2023) SARS-CoV-2 seroprevalence in children worldwide: A systematic review and meta-analysis. eClinicalMedicine (https://doi.org/10.1016/j.eclinm.2022.101786)
32 –Alizadeh Khatir A., Sepidarkish M., Daryabari Y., Taghipour A., Mollalo A., Aghapour S., Rostami A. (2023) Malaria infection and the risk of epilepsy: a meta-analysis. Parasitology 150(4):382-390 (https://doi.org/10.1017/S0031182022001780)
31 – Tabasi M., Alesheikh A.A., Kalantari M., Babaie E., Mollalo A.* (2022) Spatial Modeling of COVID-19 Prevalence using Adaptive Neuro-Fuzzy Inference System. ISPRS International Journal of Geo-Information 15(18), 13827 (https://doi.org/10.3390/ijgi11100499)
30 – Holland C., Sepidrakhsh M., Deslyper G., Abdollahi A., Valizadeh S., Mollalo A., Mahjour S., Ghodsian S., Ardekani A., Behniafar H., Gasser R.*, Rostami A.* (2022) Global Prevalence of Ascaris Infection in Humans (2010-2021): A Systematic Review and Meta-analysis. Infectious Diseases of Poverty 11(1), 113 (http://dx.doi.org/10.1186/s40249-022-01038-z)
29 – Rivera K.M., Mollalo A.* (2022) Spatial Analysis and Modeling of Depression Relative to Social Vulnerability Index Across the United States. Geospatial Health 17, 1132 (https://doi.org/10.4081/gh.2022.1132)
28 – Ardekani A., Taherifard E., Mollalo A., Hemadi E., Roshanshad A., Fereidooni R., Rouholamin S., Rezaeinejad M., Farid-Mojtahedi M., Razavi M., Rostami A.* (2022) Human papillomavirus infection during pregnancy and childhood: a comprehensive review. Microorganisms (https://doi.org/10.3390/microorganisms10101932)
27 – Shiadeh M.N.*, Sepidarkish M., Mollalo A., As’adi N., Khani S., Shahhosseini Z., Danesh M., Esfandyari S., Mokdad A.H., Rostami A. (2022) Worldwide prevalence of maternal methicillin-resistant Staphylococcus aureus colonization: A systematic review and meta-analysis. Microbial Pathogenesis 171, 105743 (https://doi.org/10.1016/j.micpath.2022.105743)
26 – Vaezzadeh K., Sepidarkish M.*, Mollalo A., As’adi N., Rouholamin S., Rezaeinejad M., Mojtahedi M.F., Hosseini S.M.M., Taheri M., Mahjour S., Mohammadi M., Chemaitelly H., Rostami A.* (2022) Global prevalence of Neisseria gonorrhoeae infection in pregnant women: a systematic review and meta-analysis. Clinical Microbiology and Infection (https://doi.org/10.1016/j.cmi.2022.08.008)
25 – Rostami A.*, Riahi S.M., Mollalo A., Razavian I., Akbari N., Marhoommirzabak E., Mahjour S., Sartip B., Arshadi M., Razavian E., Ardekani A. (2022) Does latent Toxoplasma infection have a protective effect against developing multiple sclerosis? Evidence from an updated meta-analysis. Transactions of The Royal Society of Tropical Medicine and Hygiene (https://doi.org/10.1093/trstmh/trac053)
24 – Sorkhi H., Mollalo A., Bijani A., Mehravar S., Pournasrollah M., Moharerpour S., Rostami A.* (2022) Association between Toxoplasma gondii Infection and Nephrotic Syndrome Risk in Children: A Case-Control Study and Systematic Review. Journal of Tropical Pediatrics 68, fmac067 (https://doi.org/10.1093/tropej/fmac067)
23 – Ardekani A., Sepidarkish M., Mollalo A., Afradiasbagharani P., Rouholamin S., Rezaeinejad M., Farid‐Mojtahedi M., Mahjour S., Almukhtar M., Nourollahpour Shiadeh M*, Rostami A.* (2022) Worldwide prevalence of human papillomavirus among pregnant women: A systematic review and meta‐analysis, Reviews in Medical Virology, e2374 (https://doi.org/10.1002/rmv.2374)
22 – Kianfar N.*, Mesgari M.S., Mollalo A., Kaveh, M. (2022) Spatio-temporal modeling of COVID-19 prevalence and mortality using artificial neural network algorithms. Spatial and Spatio-temporal Epidemiology 40, 100471 (https://doi.org/10.1016/j.sste.2021.100471)
21 – Mollalo A., Mohammadi A., Mavaddati S., Kiani B.* (2021) Spatial analysis of COVID-19 vaccination: A scoping review. International Journal of Environmental Research and Public Health 18(22), 12024 (https://doi.org/10.3390/ijerph182212024)
20 – Abedi S.H., Fazlzadeh A., Mollalo A., Sartip B., Mahjour S., Bahadory S., Taghipour A., Rostami A.* (2021) The neglected role of Blastocystis sp. and Giardia lamblia in development of irritable bowel syndrome: A systematic review and meta-analysis. Microbial Pathogenesis 162, 105215 (https://doi.org/10.1016/j.micpath.2021.105215)
19 – Rostami A.*, Sepidarkish M., Fazlzadeh A., Mokdad A.H., Sattarnezhad A., Esfandyari S., Riahi S.M., Mollalo A., Dooki M.E., Bayani M., Nazemipour M., Mansournia M.A., Hotez P.J., Gasser R.B. (2021) Update on SARS-Cov-2 seroprevalence – regional and worldwide. Clinical Microbiology and Infection 27(12), 1762-1771 (https://doi.org/10.1016/j.cmi.2021.09.019)
18 – Mollalo A. *, Tatar M. (2021) Spatial Modeling of COVID-19 Hesitancy in the United States. International Journal of Environmental Research and Public Health 18(18), 9488 (https://doi.org/10.3390/ijerph18189488)
17 – Mohammadi A., Mollalo A., Bergquist R., Kiani B.* (2021) Measuring COVID-19 vaccination coverage: An enhanced age-adjusted two-step floating catchment area model. Infectious Diseases of Poverty 10, 118 (https://doi.org/10.1186/s40249-021-00904-6)
16 – Rostami A.*, Riahi S.M., Esfandyari S., Habibpour H., Mollalo A., Mirzapour A., Behniafar H., Moghaddam S., Kyvanani N.A., Aghaei S., Bazrafshan N., Ghazvini S. (2021) Geo-climatic factors and prevalence of chronic toxoplasmosis in pregnant women: A meta-analysis and meta-regression. Environmental Pollution 228, 117790 (https://doi.org/10.1016/j.envpol.2021.117790)
15 – Varzegar P., Bayani M.*, Kalantari N., Nasiri-Kenari M., Amini B.N., Mollalo A., Rostami A.* (2021) Seroprevalence of Strongyloides stercoralis among patients with leptospirosis in north of Iran: a descriptive cross-sectional study, Journal of Helminthology 95, e34 (https://doi.org/10.1017/S0022149X21000237)
14 – Mollalo A.*, Rivera, K. M., Vahabi N. (2021) Spatial Statistical Analysis of COVID-19 Mortalities in the Continental United States, Sustainable Cities and Society 67, 102738 (https://doi.org/10.1016/j.scs.2021.102738)
13 – Vahabi N., Salehi M., Duarte J.D., Mollalo A., Michailidis G.* (2021) County-Level Longitudinal Clustering of COVID-19 Mortality to Incidence Ratio in the United States, Scientific Reports 11, 3088 (https://doi.org/10.1038/s41598-021-82384-0)
12 – Mollalo A.*, Vahedi B., Bhattarai S., Hopkins L.C., Banik S., Vahedi B. (2020) Predicting the hotspots of age-adjusted mortality rates of lower respiratory infection across the continental United States: integration of GIS, spatial statistics and machine learning algorithms. International Journal of Medical Informatics 142, 104248 (https://doi.org/10.1016/j.ijmedinf.2020.104248)
11 – Mollalo A.*, Rivera, K. M., Vahedi B. (2020) Artificial neural network modeling of novel coronavirus (COVID-19) incidence rates across the continental United States. International Journal of Environmental Research and Public Health 17(12), 4204 (https://doi.org/10.3390/ijerph17124204)
10 – Mollalo A.*, Vahedi, B., & Rivera, K. M. (2020) GIS-based spatial modeling of COVID-19 incidence rate in the continental United States. Science of The Total Environment 728, 138884 (https://doi.org/10.1016/j.scitotenv.2020.138884)
9 – Mollalo A.*, Mao L., Rashidi P., Glass G.E. (2019) A GIS-based Artificial Neural Network Model for Predicting Spatial Distribution of Tuberculosis Incidence Rate across the United States, International Journal of Environmental Research and Public Health 16(1), 157 (https://doi.org/10.3390/ijerph16010157)
8 – Mollalo A.*, Sadeghian A., Israel G.D., Rashidi P., Sofizadeh A., Glass G.E. (2018) Machine learning approaches in GIS-based ecological modeling of the sand fly Phlebotomus papatasi, a vector of zoonotic cutaneous leishmaniasis, Acta Tropica 188, 187-194 (https://doi.org/10.1016/j.actatropica.2018.09.004)
7 – Mollalo A.*, Blackburn J.K., Morris L.R., Glass G.E. (2017) A 24-Year Exploratory Spatial Data Analysis of Lyme Disease Incidence Rate in Connecticut, Geospatial Health 12(2), 558 (https://doi.org/10.4081/gh.2017.588)
6 – Sofizadeh A., Rassi Y., Vatandoost H.*, Hanafi-Bojd A.A., Mollalo A., Rafizadeh S., Akhavan A.A. (2017) Predicting the Distribution of Phlebotomus papatasi (Diptera: Psychodidae) the Primary Vector of Zoonotic Cutaneous Leishmaniasis in Golestan Province of Iran Using Ecological Niche Modeling: Comparison of MaxEnt and GARP Models. Journal of Medical Entomology 54(2), 312-320 (https://doi.org/10.1093/jme/tjw178)
5 – Mollalo A.*, Khodabandehloo E. (2016) Zoonotic Cutaneous Leishmaniasis in Northeastern Iran: A GIS-based Spatio-temporal Multi-Criteria Decision-Making Approach. Epidemiology and Infection 144(10), 2217–2229 (http://dx.doi.org/10.1017/S0950268816000224)
4 – Mollalo A.*, Alimohammadi A., Shirzadi M.R., Malek M.R. (2015) Geographic Information System-based Analysis of the Spatial and Spatio-Temporal Distribution of Zoonotic Cutaneous Leishmaniasis in Golestan Province, North-east of Iran, Zoonosis and Public Health 62(1), 18-28 (http://dx.doi.org/10.1111/zph.12109)
3 – Shirzadi M.R., Mollalo A.*, Yaghoobi-Ershadi M.R. (2015) Dynamic Relations between Incidence of Zoonotic Cutaneous Leishmaniasis and Climatic Factors in an Endemic Focus of Iran, Journal of Arthropod-borne Diseases 9(2), 148-160 (https://pubmed.ncbi.nlm.nih.gov/26623427)
2 – Mollalo A.*, Alimohammadi A., Khoshabi M. (2014) Spatial and Spatio-Temporal Analysis of the Human Brucellosis in Iran, Transactions of the Royal Society of Tropical Medicine and Hygiene 108(11), 721-728 (http://dx.doi.org/10.1093/trstmh/tru133)
1 – Mollalo A.*, Alimohammadi A., Shahrisvand M., Shirzadi M.R., Malek M.R. (2014) Spatial and Statistical Analyses of the Relations between Vegetation Cover and Incidence of Cutaneous Leishmaniasis in an Endemic Province, Northeast of Iran, Asian Pacific Journal of Tropical Disease 4(1), 930-934 (http://dx.doi.org/10.1016/S2222-1808(14)60500-4)
BOOK CHAPTER
Tehrani N.A., Mollalo A., Farhanj F., Pahlevanzadeh N., Janalipour M. (2021) Time-Series Analysis of COVID-19 in Iran: A Remote Sensing Perspective. In Rjabifard A., Paez D., Foliente G. (Eds). COVID-19: Geospatial Information and Community Resilience, Taylor & Francis Group (https://doi.org/10.1201/9781003181590)
PRESENTATIONS (*Invited Talks)
Mollalo A. Factors Associated with Alzheimer's Disease Dementia Prevalence in the United States: A County-Level Spatial Machine Learning Analysis. HML Science Day. Charleston, South Carolina, October 3, 2024.
Yang D., Huang X., He Y., Mollalo A., Low R. Advancing Mosquito-Borne Disease Prediction: Bringing together GLOBE data, NASA observations, and AI models, SANO, Lima, Peru, October 1, 2024.
Mollalo A. GIS-based Spatial Modeling of Alzheimer’s Dementia Prevalence Across the US, Hollings Marine Lab Science Day, Charleston, South Carolina, October 3, 2024.
Mollalo A., Spatial Modeling of HPV Vaccination among Children in North Carolina, American Association of Geographers (AAG), Honolulu, Hawaii, April 16-20, 2024.
Mollalo A., Deep Neural Network in Modeling Depression in the United States, AAG, Boulder, Colorado, March 23-27, 2023.
Mollalo A., Measuring COVID-19 Vaccination Coverage to Support Healthcare Equity Decision-Making in Urban Areas, AAG , New York City, February 25-March 1, 2022. (Accepted)
Mollalo A., Spatial Modeling of COVID-19 Morbidity/Mortality using Machine-Learning Algorithms, The American Geophysical Union (AGU), New Orleans, Louisiana, December 13-17, 2021.
Mollalo A.*, “Spatial Variations of the COVID-19 Incidence in the United States: A GIS-based Approach”, University of Tennessee, Health Science Center, Biostatistics Seminar Series, July 27, 2020.
Mollalo A., Application of Machine Learning Models in Predicting the Hotspots of Age-Adjusted Mortality Rates of Lower Respiratory Infection, USA, Ohio Public Health Academic Forum (Virtual Conference), April 22-29, 2020.
Mollalo A., Spatial Autoregressive Modeling of Lyme disease Incidence: A Statewide Study in New York (2000-2010), Florida Society of Geographers, Melbourne, Florida, February 9-11, 2018.
Mollalo A., A GIS-based Machine Learning Technique for Predicting Spatial Distribution of Phlebotomus papatasi (Diptera: Psychodidae), the Main Vectors of Zoonotic Cutaneous Leishmaniasis, American Association of Geographers (AAG), New Orleans, Louisiana, April 10-14, 2018.
Mollalo A.*, “Agent-based Modeling in Public Health Using NetLogo”, University of Florida, March 22, 2017.
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