ABSTRACT
OBJECTIVES: To identify studies promoting the use of artificial intelligence (AI) or automation with HIV pre-exposure prophylaxis (PrEP) care and explore ways for AI to be used in PrEP interventions. DESIGN: Systematic review. METHODS: We searched in the US Centers for Disease Control and Prevention Research Synthesis database through November 2023 PROSPERO (CRD42023458870). We included studies published in English that reported using AI or automation in PrEP interventions. Two reviewers independently reviewed the full text and extracted data by using standard forms. Risk of bias was assessed using either the revised Cochrane risk-of-bias tool for randomized trials for randomized controlled trials or an adapted Newcastle-Ottawa Quality Assessment Scale for non-randomized studies. RESULTS: Our search identified 12 intervention studies (i.e., interventions that used AI/automation to improve PrEP care). Currently available intervention studies showed AI/automation interventions were acceptable and feasible in PrEP care while improving PrEP-related outcomes (i.e., knowledge, uptake, adherence, discussion with care providers). These interventions have used AI/automation to reduce workload (e.g., directly observed therapy) and helped non-HIV specialists prescribe PrEP with AI-generated clinical decision-support. Automated tools can also be developed with limited budget and staff experience. CONCLUSIONS: AI and automation have high potential to improve PrEP care. Despite limitations of included studies (e.g., the small sample sizes and lack of rigorous study design), our review suggests that by using aspects of AI and automation appropriately and wisely, these technologies may accelerate PrEP use and reduce HIV infection.
ABSTRACT
The rapid spread of the numerous outbreaks of the coronavirus disease 2019 (COVID-19) pandemic has fueled interest in mathematical models designed to understand and predict infectious disease spread, with the ultimate goal of contributing to the decision making of public health authorities. Here, we propose a computational pipeline that dynamically parameterizes a modified SEIRD (susceptible-exposed-infected-recovered-deceased) model using standard daily series of COVID-19 cases and deaths, along with isolated estimates of population-level seroprevalence. We test our pipeline in five heavily impacted states of the US (New York, California, Florida, Illinois, and Texas) between March and August 2020, considering two scenarios with different calibration time horizons to assess the update in model performance as new epidemiologic data become available. Our results show a median normalized root mean squared error (NRMSE) of 2.38% and 4.28% in calibrating cumulative cases and deaths in the first scenario, and 2.41% and 2.30% when new data are assimilated in the second scenario, respectively. Then, 2-week (4-week) forecasts of the calibrated model resulted in median NRMSE of cumulative cases and deaths of 5.85% and 4.68% (8.60% and 17.94%) in the first scenario, and 1.86% and 1.93% (2.21% and 1.45%) in the second. Additionally, we show that our method provides significantly more accurate predictions of cases and deaths than a constant parameterization in the second scenario (p < 0.05). Thus, we posit that our methodology is a promising approach to analyze the dynamics of infectious disease outbreaks, and that our forecasts could contribute to designing effective pandemic-arresting public health policies. Supplementary Information: The online version contains supplementary material available at 10.1007/s00366-023-01816-9.
