@article{nokey,

title = {Why Similar Policies Resulted In Different COVID-19 Outcomes: How Responsiveness And Culture Influenced Mortality Rates},

author = {Tse Yang Lim and Ran Xu and Nick Ruktanonchai and Omar Saucedo and Lauren M Childs and Mohammad S Jalali and Hazhir Rahmandad and Navid Ghaffarzadegan},

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2023/12/lim-et-al-2023-HealthAffairs.pdf},

year  = {2023},

date = {2023-12-06},

urldate = {2023-12-06},

journal = {Health Affairs},

volume = {42},

issue = {12},

abstract = {In the first two years of the COVID-19 pandemic, per capita mortality varied by more than a hundredfold across countries, despite most implementing similar nonpharmaceutical interventions. Factors such as policy stringency, gross domestic product, and age distribution explain only a small fraction of mortality variation. To address this puzzle, we built on a previously validated pandemic model in which perceived risk altered societal responses affecting SARS-CoV-2 transmission. Using data from more than 100 countries, we found that a key factor explaining heterogeneous death rates was not the policy responses themselves but rather variation in responsiveness. Responsiveness measures how sensitive communities are to evolving mortality risks and how readily they adopt nonpharmaceutical interventions in response, to curb transmission. We further found that responsiveness correlated with two cultural constructs across countries: uncertainty avoidance and power distance. Our findings show that more responsive adoption of similar policies saves many lives, with important implications for the design and implementation of responses to future outbreaks.},

keywords = {Infectious diseases},

pubstate = {published},

tppubtype = {article}

}

@article{Yildirim2023,

title = {Increasing the initiation of antiretroviral therapy through optimal placement of diagnostic technologies for pediatric HIV in Zimbabwe: a modeling analysis},

author = {Melike Yildirim and Karen A. Webb and Andrea L. Ciaranello and Alyssa K. Amick and Angela Mushavi and Anesu Chimwaza and Anneke Claypool and Tendayi Murape and Nicole C. McCann and Clare Flanagan and Mohammad S. Jalali

},

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2023/06/Yildirim_IJID_2023.pdf},

year  = {2023},

date = {2023-05-19},

urldate = {2023-05-19},

journal = {The International Journal of Infectious Diseases},

abstract = {Objectives: Point-of-care (POC) devices for infant HIV testing provide timely result-return and increase antiretroviral (ART) initiation. We aimed to optimally locate POC devices to increase 30-day ART initiation in Matabeleland South, Zimbabwe.

Methods: We developed an optimization model to identify the locations for limited POC devices at health facilities, maximizing the number of infants who receive HIV test results and initiate ART within 30 days of testing. We compared location-optimization model results to non-model-based decision heuristics, which are more practical and less data-intensive. Heuristics assign POC devices based on demand, test positivity, laboratory result-return probability, and POC machine functionality.

Results: With the current placement of 11 existing POC machines, 37% of all tested infants with HIV were projected to receive results and 35% were projected to initiate ART within 30 days of testing. With optimal placement of existing machines, 46% were projected to receive results and 44% to initiate ART within 30 days, retaining three machines in current locations, moving eight to new facilities. Relocation based on the highest POC device functionality would be the best-performing heuristic decision (44% receiving results and 42% initiating ART within 30 days); although, it still would not perform as well as the optimization-based approach.

Conclusion: Optimal and ad hoc heuristic relocation of limited POC machines would increase timely result-return and ART initiation, without further, often costly, interventions. Location optimization can enhance decision-making regarding the placement of medical technologies for HIV care.},

keywords = {Infectious diseases, Working papers},

pubstate = {published},

tppubtype = {article}

}

@article{650797,

title = {Readiness of healthcare providers for e-hospitals: a cross-sectional analysis in China before the COVID-19 period},

author = {Peiyi Li and Yunmei Luo and Xuexin Yu and Zhi Zeng and Jin Wen and Elizabeth Mason and Weimin Li and Mohammad S Jalali},

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2022/12/Li_2022_BMJOpen.pdf},

doi = {10.1136/bmjopen-2021-054169},

year  = {2022},

date = {2022-01-01},

journal = {BMJ Open},

volume = {12},

pages = {e054169},

keywords = {Infectious diseases},

pubstate = {published},

tppubtype = {article}

}

@article{668159,

title = {Insights into vaccine hesitancy from systems thinking, Rwanda},

author = {Catherine Decouttere and Stany Banzimana and Pal Davidsen and Carla Van Riet and Corinne Vandermeulen and Elizabeth Mason and Mohammad S. Jalali and Nico Vandaele},

year  = {2021},

date = {2021-08-03},

urldate = {2021-08-03},

keywords = {Infectious diseases},

pubstate = {published},

tppubtype = {article}

}

@article{669755,

title = {How Systems Respond to Policies: Unintended Consequences of COVID-19 Lockdown Policies in Thailand},

author = {Borwornsom Leerapan and Phanuwich S Kaewkamjornchai and Rifat A Atun and Mohammad S Jalali},

year  = {2021},

date = {2021-08-02},

keywords = {Infectious diseases},

pubstate = {published},

tppubtype = {article}

}

@article{651235,

title = {Weather, air pollution, and SARS-CoV-2 transmission: a global analysis},

author = {Ran Xu and Hazhir Rahmandad and Marichi Gupta and Catherine DiGennaro and Navid Ghaffarzadegan and Mohammad S Jalali},

year  = {2021},

date = {2021-07-29},

journal = {Lancet Planetary Health},

abstract = {Background: Understanding and projecting the spread of COVID-19 requires reliable estimates of how weather components are associated with the transmission of the virus. Prior research on this topic has been inconclusive. Identifying key challenges to reliable estimation of weather impact on transmission we study this question using one of the largest assembled databases of COVID-19 infections and weather.Methods: We assemble a dataset that includes virus transmission and weather data across 3,739 locations from December 12, 2019 to April 22, 2020. Using simulation, we identify key challenges to reliable estimation of weather impacts on transmission, design a statistical method to overcome these challenges, and validate it in a blinded simulation study. Using this method and controlling for location-specific response trends we estimate how different weather variables are associated with the reproduction number for COVID-19. We then use the estimates to project the relative weather-related risk of COVID-19 transmission across the world and in large cities.Results: We show that the delay between exposure and detection of infection complicates the estimation of weather impact on COVID-19 transmission, potentially explaining significant variability in results to-date. Correcting for that distributed delay and offering conservative estimates, we find a negative relationship between temperatures above 25 degrees Celsius and estimated reproduction number (R ̂), with each degree Celsius associated with a 3.1% (95% CI, 1.5% to 4.8%) reduction in R ̂. Higher levels of relative humidity strengthen the negative effect of temperature above 25 degrees. Moreover, one millibar of additional pressure increases R ̂ by approximately 0.8 percent (95% CI, 0.6% to 1%) at the median pressure (1016 millibars) in our sample. We also find significant positive effects for wind speed, precipitation, and diurnal temperature on R ̂. Sensitivity analysis and simulations show that results are robust to multiple assumptions. Despite conservative estimates, weather effects are associated with a 43% change in R ̂ between the 5th and 95th percentile of weather conditions in our sample.Conclusions: These results provide evidence for the relationship between several weather variables and the spread of COVID-19. However, the (conservatively) estimated relationships are not strong enough to seasonally control the epidemic in most locations. 

 

 

 

	[["fid":"715368","view_mode":"default","type":"media","attributes":"height":"498","width":"832","style":"width: 700px; height: 419px;","alt":"weather_conditions_and_covid19","class":"media-element file-default"]] 

 

 

 

	Online simulator: https://projects.iq.harvard.edu/covid19},

keywords = {Infectious diseases},

pubstate = {published},

tppubtype = {article}

}

@article{660470,

title = {Whether the Weather Will Help Us Weather the COVID-19 Pandemic: Using Machine Learning to Measure Twitter Users’ Perceptions},

author = {Marichi Gupta and Aditya Bansal and Bhav Jain and Jillian Rochelle and Atharv Oak and Mohammad S Jalali},

url = {https://scholar.harvard.edu/files/jalali/files/perceptions_weather_and_covid19.pdf},

year  = {2021},

date = {2021-01-01},

journal = {International Journal of Medical Informatics},

volume = {145},

pages = {104340},

abstract = {Objective: The potential ability for weather to affect SARS-CoV-2 transmission has been an area of controversial discussion during the COVID-19 pandemic. Individualstextquoteright perceptions of the impact of weather can inform their adherence to public health guidelines; however, there is no measure of their perceptions. We quantified Twitter userstextquoteright perceptions of the effect of weather and analyzed how they evolved with respect to real-world events and time. 

 

 

 

	Materials and Methods: We collected 166,005 tweets posted between January 23 and June 22, 2020 and employed machine learning/natural language processing techniques to filter for relevant tweets, classify them by the type of effect they claimed, and identify topics of discussion. 

 

 

 

	Results: We identified 28,555 relevant tweets and estimate that 40.4% indicate uncertainty about weathertextquoterights impact, 33.5% indicate no effect, and 26.1% indicate some effect. We tracked changes in these proportions over time. Topic modeling revealed major latent areas of discussion. 

 

 

 

	Discussion: There is no consensus among the public for weathertextquoterights potential impact. Earlier months were characterized by tweets that were uncertain of weathertextquoterights effect or claimed no effect; later, the portion of tweets claiming some effect of weather increased. Tweets claiming no effect of weather comprised the largest class by June. Major topics of discussion included comparisons to influenzatextquoterights seasonality, President Trumptextquoterights comments on weathertextquoterights effect, and social distancing. 

 

 

 

	Conclusion: There is a major gap between scientific evidence and public opinion of weathertextquoterights impacts on COVID-19. We provide evidence of publictextquoterights misconceptions and topics of discussion, which can inform public health communications.  

 

 

 

 

 

 

 

	[["fid":"715366","view_mode":"default","type":"media","attributes":"height":"713","width":"1249","style":"width: 700px; height: 400px;","alt":"Weather tweets","class":"media-element file-default"]]},

keywords = {Infectious diseases},

pubstate = {published},

tppubtype = {article}

}

@article{651958,

title = {How Telemedicine Integrated into China’s Anti-COVID-19 Strategies: Case from a National Referral Center},

author = {Peiyi Li and Xiaoyu Liu and Elizabeth Mason and Guangyu Hu and Yongzhao Zhou and Weimin Li and Mohammad S Jalali},

url = {https://scholar.harvard.edu/files/jalali/files/telemedicine_china.pdf},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {BMJ Health & Care Informatics},

number = {27},

pages = {e100164},

abstract = {Introduction We present the integration of telemedicine into the healthcare system of West China Hospital of Sichuan University (WCH), one of the largest hospitals in the world with 4300 inpatient beds, as a means for maximising the efficiency of healthcare delivery during the COVID-19 pandemic. 

 

 

 

	Methods Implemented on 22 January 2020, the telemedicine technology allowed WCH providers to conduct teleconsultations, telerounds, teleradiology and tele-intensive care unit, which in culmination provided screening, triage and treatment for COVID-19 and other illnesses. To encourage its adoption, the government and the hospital publicised the platform on social media and waived fees. 

 

 

 

	Discussion From 1 February to 1 April 2020, 10557 online COVID-19 consultations were conducted for 6662 individuals; meanwhile, 32676 patients without COVID completed virtual follow-ups. We discuss that high-quality, secure, affordable and user-friendly telemedical platforms should be integrated into global healthcare systems to help decrease the transmission of the virus and protect healthcare providers from infection. 

 

 

 

	 },

keywords = {Adoption dynamics, Infectious diseases},

pubstate = {published},

tppubtype = {article}

}

@article{651959,

title = {Transparency Assessment of COVID-19 Models},

author = {Mohammad S Jalali and Catherine DiGennaro and Devi Sridhar},

url = {https://scholar.harvard.edu/files/jalali/files/transparency_assessment_covid19.pdf

https://scholar.harvard.edu/files/jalali/files/transparency_assessment_covid19_appendix.pdf},

year  = {2020},

date = {2020-01-01},

journal = {The Lancet Global Health},

volume = {8},

number = {12},

pages = {E1459-E1460},

abstract = {As the COVID-19 pandemic has caused major societal unrest, modelers have worked to project future trends of COVID-19 and predict upcoming challenges and impacts of policy action. These models, alone or in aggregate, are influential for decision-makers at every level. Therefore, the method and documentation of COVID-19 models must be highly transparent to ensure that projections and consequential policies put forth have sound epistemological grounds. We evaluated 29 COVID-19 models receiving high attention levels within the scientific community and/or informing government responses. We evaluated these models against 27 transparency criteria. We found high levels of transparency in model documentation aspects such as reporting uncertainty analysis; however, about half of the models do not share code and a quarter do not report equations. These discrepancies underscore the need for transparency and reproducibility to be at the forefront of researcherstextquoteright priorities, especially during a global health crisis when stakes are critically high.},

keywords = {Infectious diseases, Simulation modeling},

pubstate = {published},

tppubtype = {article}

}

@article{631214,

title = {Preventive behaviors and perceptions of influenza vaccination among a university student population},

author = {Sahar Hashmi and Lisa D'Ambrosio and David V Diamond and Mohammad S Jalali and Stan N Finkelstein and Richard C Larson},

url = {https://scholar.harvard.edu/files/jalali/files/preventive_behaviors_and_perceptions_of_vaccination.pdf},

year  = {2016},

date = {2016-01-01},

journal = {Journal of Public Health},

volume = {38},

number = {4},

pages = {739–745},

abstract = {Background: Every year during influenza season, preventable illnesses occur due to lack of vaccination and failure to adopt the preventive behaviors known as non-pharmaceutical interventions (NPIs). In an effort to study the impact of preventive strategies and policies on behavioral changes during the spread of the H1N1 pandemic in 2009, we examined a sample of undergraduate, graduate and business students at the Massachusetts Institute of Technology (MIT).Methods: An online survey was completed by 653 students to assess NPI use, perceptions of influenza vaccinations and effectiveness of preventive health policy strategies during the 2009 H1N1 outbreak. Strategies included e-mails and text messages, posters in corridors and restrooms, and videos. These strategies were implemented during both the first and second waves of the 2009 H1N1 pandemic.Results: Despite the widespread campaign, fewer than half of the respondents reported modifying their behaviors. We discovered that >70% of the respondents did not practice any NPIs, and more than half showed lack of knowledge of flu vaccinations.Conclusions: Our study results indicate a need for more effective strategies to encourage NPI practices in student populations during outbreaks of infection.},

keywords = {Infectious diseases},

pubstate = {published},

tppubtype = {article}

}