ABSTRACT
BACKGROUND: Targeted surveillance allows public health authorities to implement testing and isolation strategies when diagnostic resources are limited, and can be implemented via the consideration of social network topologies. Yet, it remains unclear how to implement such surveillance and control when network data are unavailable. METHODS: We evaluated the ability of socio-demographic proxies of degree centrality to guide prioritized testing of infected individuals compared to known degree centrality. Proxies were estimated via readily-available socio-demographic variables (age, gender, marital status, educational attainment, and household size). We simulated SARS-CoV-2 epidemics via a SEIR individual-based model on two contact networks from rural Madagascar to further test the applicability of these findings to low-resource contexts. RESULTS: Targeted testing using socio-demographic proxies performed similarly to targeted testing using known degree centralities. At a low testing capacity, using the proxies reduced the infection burden by 22-33% while using 20% fewer tests, compared to random testing. By comparison, using known degree centrality reduced the infection burden by 31-44% while using 26-29% fewer tests. CONCLUSIONS: We demonstrate that incorporating social network information into epidemic control strategies is an effective countermeasure to low testing capacity and can be implemented via socio-demographic proxies when social network data are unavailable.
ABSTRACT
Globally, restrictions implemented to limit the spread of COVID-19 have highlighted deeply rooted social divisions, raising concerns about differential impacts on members of different groups. Inequalities among households of different castes are ubiquitous in certain regions of India. Drawing on a novel data set of 8,564 households in Uttar Pradesh, the authors use radar plots to examine differences between castes in rates of activity for several typical behaviors before, during, and upon lifting strict lockdown restrictions. The visualization reveals that members of all castes experienced comparable reductions in activity rates during lockdown and recovery rates following it. Nonetheless, members of less privileged castes procure water outside the household more often than their more privileged peers, highlighting an avenue of improvement for future public health efforts.
ABSTRACT
BACKGROUND: Early and appropriate use of antenatal care services is critical for reducing maternal and neonatal mortality and morbidity. Yet most women in sub-Saharan Africa, including Uganda, do not seek antenatal care until later during pregnancy. This qualitative study explored pregnant women's reliance on social ties for information about initiation of antenatal care. METHODS: We conducted semi-structured, in-depth interviews with 30 pregnant women seeking their first antenatal care visit at Kawempe Referral Hospital in Kampala, Uganda. Recruitment was done purposively to obtain variation by parity and whether women currently had a male partner. Study recruitment occurred from August 25th 2020 - October 26th, 2020. We used thematic analysis following a two-stage coding process, with both deductive and inductive codes. Deductive codes followed the key domains of social network and social support theory. RESULTS: We found that the most important source of information about antenatal care initiation was these women's mothers. Other sources included their mothers-in-law, female elders including grandmothers, and male partners. Sisters and female friends were less influential information sources about antenatal initiation. One of the primary reasons for relying on their own mothers, mothers-in-law, and elder women was due to these women's lived experience with pregnancy and childbirth. Trust in the relationship was also an important factor. Some pregnant women were less likely to rely on their sisters or female friends, either due to lack of trust or these women's lack of experience with pregnancy and childbirth. The advice that pregnant women received from their mothers and others on the ideal timing for antenatal care initiation varied significantly, including examples of misinformation about when to initiate antenatal care. Pregnant women seemed less likely to delay care when more than one social tie encouraged early antenatal care. CONCLUSIONS: Educating women's social networks, especially their mothers, mothers-in-law, and community elders, about the importance of early antenatal care initiation is a promising avenue for encouraging pregnant women to seek care earlier in pregnancy.
Subject(s)
Pregnant Women , Prenatal Care , Aged , Female , Humans , Infant, Newborn , Male , Parity , Patient Acceptance of Health Care , Pregnancy , Social Networking , UgandaABSTRACT
Globally, restrictions implemented to limit the spread of COVID-19 have highlighted deeply rooted social divisions, raising concerns about differential impacts on members of different groups. Inequalities among households of different castes are ubiquitous in certain regions of India. Drawing on a novel data set of 8,564 households in Uttar Pradesh, the authors use radar plots to examine differences between castes in rates of activity for several typical behaviors before, during, and upon lifting strict lockdown restrictions. The visualization reveals that members of all castes experienced comparable reductions in activity rates during lockdown and recovery rates following it. Nonetheless, members of less privileged castes procure water outside the household more often than their more privileged peers, highlighting an avenue of improvement for future public health efforts.
ABSTRACT
Recent controversies about wearing masks and getting vaccinated to slow the spread of coronavirus disease 2019 highlight the potential for individual rights and decision making to create widespread community-level outcomes. There is little work demonstrating the collective spillover effects of pandemic mitigation efforts. The authors contribute by visualizing the proportion of unvaccinated people who would become infected at different combinations of mask wearing and vaccination in a hypothetical community. A common pattern emerges across all assumptions: below some joint threshold of mask and vaccination rates, almost all unvaccinated people will eventually become infected, and beyond that threshold there is a steep drop leading to widespread community-level protection. What differs across settings is the timing and shape of the drop-off after crossing the threshold. The authors conclude that masking and vaccination are sensible and in the best interest of the population.
ABSTRACT
As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), artificial intelligence (AI)-including machine learning (ML) and Big Data analytics-as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic. This article provides a blend of discussions on the contribution of these digital technologies, propose several complementary and multidisciplinary techniques to combat COVID-19, offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research. First, four areas, where IoT can contribute are discussed, namely: 1) tracking and tracing; 2) remote patient monitoring (RPM) by wearable IoT (WIoT); 3) personal digital twins (PDTs); and 4) real-life use case: ICT/IoT solution in South Korea. Second, the role and novel applications of AI are explained, namely: 1) diagnosis and prognosis; 2) risk prediction; 3) vaccine and drug development; 4) research data set; 5) early warnings and alerts; 6) social control and fake news detection; and 7) communication and chatbot. Third, the main uses of robotics and drone technology are analyzed, including: 1) crowd surveillance; 2) public announcements; 3) screening and diagnosis; and 4) essential supply delivery. Finally, we discuss how distributed ledger technologies (DLTs), of which blockchain is a common example, can be combined with other technologies for tackling COVID-19.