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In this paper, a mathematical model based on a system of nonlinear parabolic partial differential equations is developed to investigate the effect of human mobility on the dynamics of coronavirus 2019 (COVID-19) disease. Positivity and boundedness of the model solutions are shown. The existence of the disease-free, the endemic equilibria, and the travelling wave solutions of the model are shown. From the numerical analysis, it is shown that human mobility plays a crucial role in the disease transmission. Therefore, interventions that affect diffusion (human mobility), such as lock-down, travel restrictions, and cessation of movement, may play a significant role in controlling and preventing the spread of COVID-19. [ABSTRACT FROM AUTHOR]

Background: Coronavirus disease 2019 (COVID-19) altered healthcare utilization patterns. However, there is a dearth of literature comparing methods for quantifying the extent to which the pandemic disrupted healthcare service provision in sub-Saharan African countries., Objective: To compare interrupted time series analysis using Prophet and Poisson regression models in evaluating the impact of COVID-19 on essential health services., Methods: We used reported data from Uganda's Health Management Information System from February 2018 to December 2020. We compared Prophet and Poisson models in evaluating the impact of COVID-19 on new clinic visits, diabetes clinic visits, and in-hospital deliveries between March 2020 to December 2020 and across the Central, Eastern, Northern, and Western regions of Uganda., Results: The models generated similar estimates of the impact of COVID-19 in 10 of the 12 outcome-region pairs evaluated. Both models estimated declines in new clinic visits in the Central, Northern, and Western regions, and an increase in the Eastern Region. Both models estimated declines in diabetes clinic visits in the Central and Western regions, with no significant changes in the Eastern and Northern regions. For in-hospital deliveries, the models estimated a decline in the Western Region, no changes in the Central Region, and had different estimates in the Eastern and Northern regions., Conclusions: The Prophet and Poisson models are useful in quantifying the impact of interruptions on essential health services during pandemics but may result in different measures of effect. Rigor and multimethod triangulation are necessary to study the true effect of pandemics on essential health services., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: The onset of COVID-19 and related policy responses made it difficult to study interactive health informatics solutions in clinical study settings. Instrumented log and event data from interactive systems capture temporal details that can be used to generate insights about care continuity during ongoing pandemics., Objective: To investigate user interactions with a digital health wallet (DHW) system for addressing care continuity challenges in chronic disease management in the context of an ongoing pandemic., Materials and Methods: We analyzed user interaction log data generated by clinicians, nurses, and patients from the deployment of a DHW in a feasibility study conducted during the COVID-19 pandemic in Kenya. We used the Hamming distance from Information Theory to quantify deviations of usage patterns extracted from the events data from predetermined workflow sequences supported by the platform., Results: Nurses interacted with all the user interface elements relevant to triage. Clinicians interacted with only 43% of elements relevant to consultation, while patients interacted with 67% of the relevant user interface elements. Nurses and clinicians deviated from the predetermined workflow sequences by 42% and 36%, respectively. Most deviations pertained to users going back to previous steps in their usage workflow., Conclusions: User interaction log analysis is a valuable alternative method for generating and quantifying user experiences in the context of ongoing pandemics. However, researchers should mitigate the potential disruptions of the actual use of the studied technologies as well as use multiple approaches to investigate user experiences of health technology during pandemics., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

The use of epidemiological models for decision-making has been prominent during the COVID-19 pandemic. Our work presents the application of nonparametric Bayesian techniques for inferring epidemiological model parameters based on available data sets published during the pandemic, towards enabling predictions under uncertainty during emerging pandemics. We present a methodology and framework that allows epidemiological model drivers to be integrated as input into the model calibration process. We demonstrate our methodology using the stringency index and mobility data for COVID-19 on an SEIRD compartmental model for selected US states. Our results directly compare the use of Bayesian nonparametrics for model predictions based on best parameter estimates with results of inference of parameter values across the US states. The proposed methodology provides a framework for What-If analysis and sequential decision-making methods for disease intervention planning and is demonstrated for COVID-19, while also applicable to other infectious disease models., (©2021 AMIA - All rights reserved.)

Background: There is a growing body of evidence suggesting not all craniotomy patients require postoperative intensive care., Objective: To devise and implement a standardized protocol for craniotomy patients eligible to transition directly from the operating room to the ward-the Non-Intensive CarE (NICE) protocol., Methods: We preoperatively identified patients undergoing elective craniotomy for simple neurosurgical procedures with age <65 yr and American Society of Anesthesiologists (ASA) class of 1, 2 or 3. Postoperative eligibility was confirmed by the surgical and anesthesia teams. Upon arrival to the ward, patients were staffed with a neuroscience nurse for hourly neurological examinations for the first 8 h. Patient demographics, clinical characteristics, and outcomes were prospectively collected to evaluate the NICE protocol., Results: From February 2018 to 2019, 63 patients were included in the NICE protocol with a median age of 46 yr and 65% female predominance. Of the operations performed, 38.1% were microvascular decompressions, 31.7% were craniotomy for tumor, 15.9% were cavernous malformation resections, and 14.3% were Chiari decompressions. No patients required transfer to the intensive care unit (ICU). Median length of stay was 2 d. There was an 11.1% overall readmission rate within the median follow-up period of 48 d. Three patients (4.8%) required reoperation at time of readmission within the follow-up period (1 postoperative subdural hematoma, 2 cerebrospinal fluid leak repair). None of these complications could have been identified with a postoperative ICU stay., Conclusion: In our pilot trial of the NICE protocol, no patients required postoperative transfer to the ICU., (© Congress of Neurological Surgeons 2021.)

The Coronavirus disease 2019 (COVID-19) global pandemic has transformed almost every facet of human society throughout the world. Against an emerging, highly transmissible disease, governments worldwide have implemented non-pharmaceutical interventions (NPIs) to slow the spread of the virus. Examples of such interventions include community actions, such as school closures or restrictions on mass gatherings, individual actions including mask wearing and self-quarantine, and environmental actions such as cleaning public facilities. We present the Worldwide Non-pharmaceutical Interventions Tracker for COVID-19 (WNTRAC), a comprehensive dataset consisting of over 6,000 NPIs implemented worldwide since the start of the pandemic. WNTRAC covers NPIs implemented across 261 countries and territories, and classifies NPIs into a taxonomy of 16 NPI types. NPIs are automatically extracted daily from Wikipedia articles using natural language processing techniques and then manually validated to ensure accuracy and veracity. We hope that the dataset will prove valuable for policymakers, public health leaders, and researchers in modeling and analysis efforts to control the spread of COVID-19.

Poor communication of medication administration instructions is a preventable cause of medication nonadherence. The Universal Medication Schedule (UMS) framework improves adherence by providing a simplified set of dose timing rules. However, this framework does not readily generalize to individuals with varying daily routines. We propose a point-of-care solution for enhancing guideline-based electronic prescribing and personalizing dose schedules. We describe a JSON-based approach to encode and execute standard treatment guidelines to support electronic prescribing as well as an algorithm for optimizing medication administration schedules based on a patient's daily routine. We evaluated the structure and accuracy of our JavaScript Object Notation (JSON) formalism focusing on Kenya's hypertension treatment guidelines. Our experiments compare the medication schedules generated by our algorithm with those generated by pharmacists. Our findings show that treatment guidelines can be efficiently represented and executed using the JSON formalism, and that different medication administration schedules can be generated automatically and optimized for patients' daily routines.

Arthropod-borne viruses are a major constituent of emerging infectious diseases worldwide, but limited data are available on the prevalence, distribution, and risk factors for transmission in Kenya and East Africa. In this study, we used 1,091 HIV-negative blood specimens from the 2007 Kenya AIDS Indicator Survey (KAIS 2007) to test for the presence of IgG antibodies to dengue virus (DENV), chikungunya virus (CHIKV) and Rift Valley fever virus (RVFV).The KAIS 2007 was a national population-based survey conducted by the Government of Kenya to provide comprehensive information needed to address the HIV/AIDS epidemic. Antibody testing for arboviruses was performed on stored blood specimens from KAIS 2007 through a two-step sandwich IgG ELISA using either commercially available kits or CDC-developed assays. Out of the 1,091 samples tested, 210 (19.2%) were positive for IgG antibodies against at least one of the three arboviruses. DENV was the most common of the three viruses tested (12.5% positive), followed by RVFV and CHIKV (4.5% and 0.97%, respectively). For DENV and RVFV, the participant's province of residence was significantly associated (P≤.01) with seropositivity. Seroprevalence of DENV and RVFV increased with age, while there was no correlation between province of residence/age and seropositivity for CHIKV. Females had twelve times higher odds of exposure to CHIK as opposed to DENV and RVFV where both males and females had the same odds of exposure. Lack of education was significantly associated with a higher odds of previous infection with either DENV or RVFV (p <0.01). These data show that a number of people are at risk of arbovirus infections depending on their geographic location in Kenya and transmission of these pathogens is greater than previously appreciated. This poses a public health risk, especially for DENV.

The public health and development communities understand clearly the need to integrate anti-poverty efforts with HIV/AIDS programs. This article reports findings about the impact of the Poverty Reduction Strategy Paper (PRSP) process on Malawi's National HIV/AIDS Strategic Framework (NSF). In this article we ask, how does the PRSP process support NSF accountability, participation, access to information, funding, resource planning and allocation, monitoring, and evaluation? In 2007, we developed and conducted a survey of Malawian government ministries, United Nations agencies, members of the Country Coordination Mechanism, the Malawi National AIDS Commission (NAC), and NAC grantees (N = 125, 90% response rate), seeking survey respondents' retrospective perceptions of NSF resource levels, participation, inclusion, and governance before, during, and after Malawi's PRSP process (2000-2004). We also assessed principle health sector and economic indicators and budget allocations for HIV/AIDS. These indicators are part of a new conceptual framework called shared health governance (SHG), which seeks congruence among the values and goals of different groups and actors to reflect a common purpose. Under this framework, global health policy should encompass: (i) consensus among global, national, and sub-national actors on goals and measurable outcomes; (ii) mutual collective accountability; and (iii) enhancement of individual and group health agency. Indicators to assess these elements included: (i) goal alignment; (ii) adequate resource levels; (iii) agreement on key outcomes and indicators for evaluating those outcomes; (iv) meaningful inclusion and participation of groups and institutions; (v) special efforts to ensure participation of vulnerable groups; and (vi) effectiveness and efficiency measures. Results suggest that the PRSP process supported accountability for NSF resources. However, the process may have marginalized key stakeholders, potentially undercutting the implementation of HIV/AIDS Action Plans., (Copyright © 2010. Published by Elsevier Ltd.)