Impact of urban structure on infectious disease spreading.

The ongoing SARS-CoV-2 pandemic has been holding the world hostage for several years now. Mobility is key to viral spreading and its restriction is the main non-pharmaceutical interventions to fight the virus expansion. Previous works have shown a connection between the structural organization of cities and the movement patterns of their residents. This puts urban centers in the focus of epidemic surveillance and interventions. Here we show that the organization of urban flows has a tremendous impact on disease spreading and on the amenability of different mitigation strategies. By studying anonymous and aggregated intra-urban flows in a variety of cities in the United States and other countries, and a combination of empirical analysis and analytical methods, we demonstrate that the response of cities to epidemic spreading can be roughly classified in two major types according to the overall organization of those flows. Hierarchical cities, where flows are concentrated primarily between mobility hotspots, are particularly vulnerable to the rapid spread of epidemics. Nevertheless, mobility restrictions in such types of cities are very effective in mitigating the spread of a virus. Conversely, in sprawled cities which present many centers of activity, the spread of an epidemic is much slower, but the response to mobility restrictions is much weaker and less effective. Investing resources on early monitoring and prompt ad-hoc interventions in more vulnerable cities may prove helpful in containing and reducing the impact of future pandemics.

Ramadan is not associated with increased infection risk in Pakistani and Bangladeshi populations: Findings from controlled interrupted time series analysis of UK primary care data.

BACKGROUND: The effect of fasting on immunity is unclear. Prolonged fasting is thought to increase the risk of infection due to dehydration. This study describes antibiotic prescribing patterns before, during, and after Ramadan in a primary care setting within the Pakistani and Bangladeshi populations in the UK, most of whom are Muslims, compared to those who do not observe Ramadan. METHOD: Retrospective controlled interrupted time series analysis of electronic health record data from primary care practices. The study consists of two groups: Pakistanis/Bangladeshis and white populations. For each group, we constructed a series of aggregated, daily prescription data from 2007 to 2017 for the 30 days preceding, during, and after Ramadan, respectively. FINDINGS: Controlling for the rate in the white population, there was no evidence of increased antibiotic prescription in the Pakistani/Bangladeshi population during Ramadan, as compared to before Ramadan (IRR: 0.994; 95% CI: 0.988-1.001, p = 0.082) or after Ramadan (IRR: 1.006; 95% CI: 0.999-1.013, p = 0.082). INTERPRETATION: In this large, population-based study, we did not find any evidence to suggest that fasting was associated with an increased susceptibility to infection.

When and why direct transmission models can be used for environmentally persistent pathogens.

Variants of the susceptible-infected-removed (SIR) model of Kermack & McKendrick (1927) enjoy wide application in epidemiology, offering simple yet powerful inferential and predictive tools in the study of diverse infectious diseases across human, animal and plant populations. Direct transmission models (DTM) are a subset of these that treat the processes of disease transmission as comprising a series of discrete instantaneous events. Infections transmitted indirectly by persistent environmental pathogens, however, are examples where a DTM description might fail and are perhaps better described by models that comprise explicit environmental transmission routes, so-called environmental transmission models (ETM). In this paper we discuss the stochastic susceptible-exposed-infected-removed (SEIR) DTM and susceptible-exposed-infected-removed-pathogen (SEIR-P) ETM and we show that the former is the timescale separation limit of the latter, with ETM host-disease dynamics increasingly resembling those of a DTM when the pathogen's characteristic timescale is shortened, relative to that of the host population. Using graphical posterior predictive checks (GPPC), we investigate the validity of the SEIR model when fitted to simulated SEIR-P host infection and removal times. Such analyses demonstrate how, in many cases, the SEIR model is robust to departure from direct transmission. Finally, we present a case study of white spot disease (WSD) in penaeid shrimp with rates of environmental transmission and pathogen decay (SEIR-P model parameters) estimated using published results of experiments. Using SEIR and SEIR-P simulations of a hypothetical WSD outbreak management scenario, we demonstrate how relative shortening of the pathogen timescale comes about in practice. With atttempts to remove diseased shrimp from the population every 24h, we see SEIR and SEIR-P model outputs closely conincide. However, when removals are 6-hourly, the two models' mean outputs diverge, with distinct predictions of outbreak size and duration.

Vaccine Coverage Across the Life Course in Michigan During the COVID-19 Pandemic: January‒September 2020.

Objectives. To assess the impact of the COVID-19 pandemic on immunization services across the life course. Methods. In this retrospective study, we used Michigan immunization registry data from 2018 through September 2020 to assess the number of vaccine doses administered, number of sites providing immunization services to the Vaccines for Children population, provider location types that administer adult vaccines, and vaccination coverage for children. Results. Of 12 004 384 individual vaccine doses assessed, 48.6%, 15.6%, and 35.8% were administered to children (aged 0-8 years), adolescents (aged 9-18 years), and adults (aged 19‒105 years), respectively. Doses administered overall decreased beginning in February 2020, with peak declines observed in April 2020 (63.3%). Overall decreases in adult doses were observed in all settings except obstetrics and gynecology provider offices and pharmacies. Local health departments reported a 66.4% decrease in doses reported. For children, the total number of sites administering pediatric vaccines decreased while childhood vaccination coverage decreased 4.4% overall and 5.8% in Medicaid-enrolled children. Conclusions. The critical challenge is to return to prepandemic levels of vaccine doses administered as well as to catch up individuals for vaccinations missed. (Am J Public Health. 2021;111(11):2027-2035. https://doi.org/10.2105/AJPH.2021.306474).

Changes in notifiable infectious disease incidence in China during the COVID-19 pandemic.

Nationwide nonpharmaceutical interventions (NPIs) have been effective at mitigating the spread of the novel coronavirus disease (COVID-19), but their broad impact on other diseases remains under-investigated. Here we report an ecological analysis comparing the incidence of 31 major notifiable infectious diseases in China in 2020 to the average level during 2014-2019, controlling for temporal phases defined by NPI intensity levels. Respiratory diseases and gastrointestinal or enteroviral diseases declined more than sexually transmitted or bloodborne diseases and vector-borne or zoonotic diseases. Early pandemic phases with more stringent NPIs were associated with greater reductions in disease incidence. Non-respiratory diseases, such as hand, foot and mouth disease, rebounded substantially towards the end of the year 2020 as the NPIs were relaxed. Statistical modeling analyses confirm that strong NPIs were associated with a broad mitigation effect on communicable diseases, but resurgence of non-respiratory diseases should be expected when the NPIs, especially restrictions of human movement and gathering, become less stringent.

Network percolation reveals adaptive bridges of the mobility network response to COVID-19.

Human mobility is crucial to understand the transmission pattern of COVID-19 on spatially embedded geographic networks. This pattern seems unpredictable, and the propagation appears unstoppable, resulting in over 350,000 death tolls in the U.S. by the end of 2020. Here, we create the spatiotemporal inter-county mobility network using 10 TB (Terabytes) trajectory data of 30 million smart devices in the U.S. in the first six months of 2020. We investigate the bond percolation process by removing the weakly connected edges. As we increase the threshold, the mobility network nodes become less interconnected and thus experience surprisingly abrupt phase transitions. Despite the complex behaviors of the mobility network, we devised a novel approach to identify a small, manageable set of recurrent critical bridges, connecting the giant component and the second-largest component. These adaptive links, located across the United States, played a key role as valves connecting components in divisions and regions during the pandemic. Beyond, our numerical results unveil that network characteristics determine the critical thresholds and the bridge locations. The findings provide new insights into managing and controlling the connectivity of mobility networks during unprecedented disruptions. The work can also potentially offer practical future infectious diseases both globally and locally.

The Infectious Disease Ontology in the age of COVID-19.

BACKGROUND: Effective response to public health emergencies, such as we are now experiencing with COVID-19, requires data sharing across multiple disciplines and data systems. Ontologies offer a powerful data sharing tool, and this holds especially for those ontologies built on the design principles of the Open Biomedical Ontologies Foundry. These principles are exemplified by the Infectious Disease Ontology (IDO), a suite of interoperable ontology modules aiming to provide coverage of all aspects of the infectious disease domain. At its center is IDO Core, a disease- and pathogen-neutral ontology covering just those types of entities and relations that are relevant to infectious diseases generally. IDO Core is extended by disease and pathogen-specific ontology modules. RESULTS: To assist the integration and analysis of COVID-19 data, and viral infectious disease data more generally, we have recently developed three new IDO extensions: IDO Virus (VIDO); the Coronavirus Infectious Disease Ontology (CIDO); and an extension of CIDO focusing on COVID-19 (IDO-COVID-19). Reflecting the fact that viruses lack cellular parts, we have introduced into IDO Core the term acellular structure to cover viruses and other acellular entities studied by virologists. We now distinguish between infectious agents - organisms with an infectious disposition - and infectious structures - acellular structures with an infectious disposition. This in turn has led to various updates and refinements of IDO Core's content. We believe that our work on VIDO, CIDO, and IDO-COVID-19 can serve as a model for yielding greater conformance with ontology building best practices. CONCLUSIONS: IDO provides a simple recipe for building new pathogen-specific ontologies in a way that allows data about novel diseases to be easily compared, along multiple dimensions, with data represented by existing disease ontologies. The IDO strategy, moreover, supports ontology coordination, providing a powerful method of data integration and sharing that allows physicians, researchers, and public health organizations to respond rapidly and efficiently to current and future public health crises.

Switchover phenomenon induced by epidemic seeding on geometric networks.

It is a fundamental question in disease modeling how the initial seeding of an epidemic, spreading over a network, determines its final outcome. One important goal has been to find the seed configuration, which infects the most individuals. Although the identified optimal configurations give insight into how the initial state affects the outcome of an epidemic, they are unlikely to occur in real life. In this paper we identify two important seeding scenarios, both motivated by historical data, that reveal a complex phenomenon. In one scenario, the seeds are concentrated on the central nodes of a network, while in the second one, they are spread uniformly in the population. Comparing the final size of the epidemic started from these two initial conditions through data-driven and synthetic simulations on real and modeled geometric metapopulation networks, we find evidence for a switchover phenomenon: When the basic reproduction number [Formula: see text] is close to its critical value, more individuals become infected in the first seeding scenario, but for larger values of [Formula: see text], the second scenario is more dangerous. We find that the switchover phenomenon is amplified by the geometric nature of the underlying network and confirm our results via mathematically rigorous proofs, by mapping the network epidemic processes to bond percolation. Our results expand on the previous finding that, in the case of a single seed, the first scenario is always more dangerous and further our understanding of why the sizes of consecutive waves of a pandemic can differ even if their epidemic characters are similar.

How to better communicate the exponential growth of infectious diseases.

Exponential growth bias is the phenomenon whereby humans underestimate exponential growth. In the context of infectious diseases, this bias may lead to a failure to understand the magnitude of the benefit of non-pharmaceutical interventions. Communicating the same scenario in different ways (framing) has been found to have a large impact on people's evaluations and behavior in the contexts of social behavior, risk taking and health care. We find that framing matters for people's assessment of the benefits of non-pharmaceutical interventions. In two commonly used frames, most subjects in our experiment drastically underestimate the number of cases avoided by adopting non-pharmaceutical interventions. Framing growth in terms of doubling times rather than growth rates reduces the bias. When the scenario is framed in terms of time gained rather than cases avoided, the median subject assesses the benefit of non-pharmaceutical interventions correctly. These findings suggest changes that could be adopted to better communicate the exponential spread of infectious diseases.

Infectious disease activity during the COVID-19 epidemic in Japan: Lessons learned from prevention and control measures.

In Japan, the Law Concerning the Prevention of Infectious Diseases and Medical Care for Patients with Infectious Diseases (the "Infectious Diseases Control Law") classifies infectious diseases as category I-V infectious diseases, pandemic influenza, and designated infectious diseases based on their infectivity, severity, and impact on public health. COVID-19 was designated as a designated infectious disease as of February 1, 2020 and then classified under pandemic influenza as of February 13, 2021. According to national reports from sentinel surveillance, some infectious diseases transmitted by droplets, contact, or orally declined during the COVID-19 epidemic in Japan. As of week 22 (June 6, 2021), there were 704 cumulative cases of seasonal influenza, 8,144 cumulative cases of chickenpox, 356 cumulative cases of mycoplasma pneumonia, and 45 cumulative cases of rotavirus gastroenteritis; these numbers were significantly lower than those last year, with 563,487 cumulative cases of seasonal influenza, 31,785 cumulative cases of chickenpox, 3,518 cumulative cases of mycoplasma pneumonia, and 250 cumulative cases of rotavirus gastroenteritis. Similarly, many infectious diseases transmitted by droplets or contact declined in other countries and areas during the COVID-19 pandemic. One can reasonably assume that various measures adopted to control the transmission of COVID-19 have played a role in reducing the spread of other infectious diseases, and especially those transmitted by droplets or contact. Extensive and thorough implementation of personal protective measures and behavioral changes may serve as a valuable reference when identifying ways to reduce the spread of infectious diseases transmitted by droplets or contact in the future.

Analysis of social interactions and risk factors relevant to the spread of infectious diseases at hospitals and nursing homes.

Ensuring the safety of healthcare workers is vital to overcome the ongoing COVID-19 pandemic. We here present an analysis of the social interactions between the healthcare workers at hospitals and nursing homes. Using data from an automated hand hygiene system, we inferred social interactions between healthcare workers to identify transmission paths of infection in hospitals and nursing homes. A majority of social interactions occurred in medication rooms and kitchens emphasising that health-care workers should be especially aware of following the infection prevention guidelines in these places. Using epidemiology simulations of disease at the locations, we found no need to quarantine all healthcare workers at work with a contagious colleague. Only 14.1% and 24.2% of the health-care workers in the hospitals and nursing homes are potentially infected when we disregard hand sanitization and assume the disease is very infectious. Based on our simulations, we observe a 41% and 26% reduction in the number of infected healthcare workers at the hospital and nursing home, when we assume that hand sanitization reduces the spread by 20% from people to people and 99% from people to objects. The analysis and results presented here forms a basis for future research to explore the potential of a fully automated contact tracing systems.

Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves.

We construct a recursive Bayesian smoother, termed EpiFilter, for estimating the effective reproduction number, R, from the incidence of an infectious disease in real time and retrospectively. Our approach borrows from Kalman filtering theory, is quick and easy to compute, generalisable, deterministic and unlike many current methods, requires no change-point or window size assumptions. We model R as a flexible, hidden Markov state process and exactly solve forward-backward algorithms, to derive R estimates that incorporate all available incidence information. This unifies and extends two popular methods, EpiEstim, which considers past incidence, and the Wallinga-Teunis method, which looks forward in time. We find that this combination of maximising information and minimising assumptions significantly reduces the bias and variance of R estimates. Moreover, these properties make EpiFilter more statistically robust in periods of low incidence, where several existing methods can become destabilised. As a result, EpiFilter offers improved inference of time-varying transmission patterns that are advantageous for assessing the risk of upcoming waves of infection or the influence of interventions, in real time and at various spatial scales.

SARS-CoV-2, Zika viruses and mycoplasma: Structure, pathogenesis and some treatment options in these emerging viral and bacterial infectious diseases.

The molecular evolution of life on earth along with changing environmental, conditions has rendered mankind susceptible to endemic and pandemic emerging infectious diseases. The effects of certain systemic viral and bacterial infections on morbidity and mortality are considered as examples of recent emerging infections. Here we will focus on three examples of infections that are important in pregnancy and early childhood: SARS-CoV-2 virus, Zika virus, and Mycoplasma species. The basic structural characteristics of these infectious agents will be examined, along with their general pathogenic mechanisms. Coronavirus infections, such as caused by the SARS-CoV-2 virus, likely evolved from zoonotic bat viruses to infect humans and cause a pandemic that has been the biggest challenge for humanity since the Spanish Flu pandemic of the early 20th century. In contrast, Zika Virus infections represent an expanding infectious threat in the context of global climate change. The relationship of these infections to pregnancy, the vertical transmission and neurological sequels make these viruses highly relevant to the topics of this special issue. Finally, mycoplasmal infections have been present before mankind evolved, but they were rarely identified as human pathogens until recently, and they are now recognized as important coinfections that are able to modify the course and prognosis of various infectious diseases and other chronic illnesses. The infectious processes caused by these intracellular microorganisms are examined as well as some general aspects of their pathogeneses, clinical presentations, and diagnoses. We will finally consider examples of treatments that have been used to reduce morbidity and mortality of these infections and discuss briefly the current status of vaccines, in particular, against the SARS-CoV-2 virus. It is important to understand some of the basic features of these emerging infectious diseases and the pathogens involved in order to better appreciate the contributions of this special issue on how infectious diseases can affect human pregnancy, fetuses and neonates.

Fluid Dynamics of Respiratory Infectious Diseases.

The host-to-host transmission of respiratory infectious diseases is fundamentally enabled by the interaction of pathogens with a variety of fluids (gas or liquid) that shape pathogen encapsulation and emission, transport and persistence in the environment, and new host invasion and infection. Deciphering the mechanisms and fluid properties that govern and promote these steps of pathogen transmission will enable better risk assessment and infection control strategies, and may reveal previously underappreciated ways in which the pathogens might actually adapt to or manipulate the physical and chemical characteristics of these carrier fluids to benefit their own transmission. In this article, I review our current understanding of the mechanisms shaping the fluid dynamics of respiratory infectious diseases.

Thinking clearly about social aspects of infectious disease transmission.

Social and cultural forces shape almost every aspect of infectious disease transmission in human populations, as well as our ability to measure, understand, and respond to epidemics. For directly transmitted infections, pathogen transmission relies on human-to-human contact, with kinship, household, and societal structures shaping contact patterns that in turn determine epidemic dynamics. Social, economic, and cultural forces also shape patterns of exposure, health-seeking behaviour, infection outcomes, the likelihood of diagnosis and reporting of cases, and the uptake of interventions. Although these social aspects of epidemiology are hard to quantify and have limited the generalizability of modelling frameworks in a policy context, new sources of data on relevant aspects of human behaviour are increasingly available. Researchers have begun to embrace data from mobile devices and other technologies as useful proxies for behavioural drivers of disease transmission, but there is much work to be done to measure and validate these approaches, particularly for policy-making. Here we discuss how integrating local knowledge in the design of model frameworks and the interpretation of new data streams offers the possibility of policy-relevant models for public health decision-making as well as the development of robust, generalizable theories about human behaviour in relation to infectious diseases.

An SIR-type epidemiological model that integrates social distancing as a dynamic law based on point prevalence and socio-behavioral factors.

Modeling human behavior within mathematical models of infectious diseases is a key component to understand and control disease spread. We present a mathematical compartmental model of Susceptible-Infectious-Removed to compare the infected curves given by four different functional forms describing the transmission rate. These depend on the distance that individuals keep on average to others in their daily lives. We assume that this distance varies according to the balance between two opposite thrives: the self-protecting reaction of individuals upon the presence of disease to increase social distancing and their necessity to return to a culturally dependent natural social distance that occurs in the absence of disease. We present simulations to compare results for different society types on point prevalence, the peak size of a first epidemic outbreak and the time of occurrence of that peak, for four different transmission rate functional forms and parameters of interest related to distancing behavior, such as: the reaction velocity of a society to change social distance during an epidemic. We observe the vulnerability to disease spread of close contact societies, and also show that certain social distancing behavior may provoke a small peak of a first epidemic outbreak, but at the expense of it occurring early after the epidemic onset, observing differences in this regard between society types. We also discuss the appearance of temporal oscillations of the four different transmission rates, their differences, and how this oscillatory behavior is impacted through social distancing; breaking the unimodality of the actives-curve produced by the classical SIR-model.

Kenyan school book knowledge for water, sanitation, hygiene and health education interventions: Disconnect, integration or opportunities?

BACKGROUND: Schools, depending on their access to and quality of water, sanitation and hygiene (WASH) and the implementation of healthy behaviours, can be critical for the control and spread of many infectious diseases, including COVID-19. Schools provide opportunities for pupils to learn about the importance of hygiene and WASH-related practice, and build healthy habits and skills, with beneficial medium- and long-term consequences particularly in low- and middle-income countries: reducing pupils' absenteeism due to diseases, promoting physical, mental and social health, and improving learning outcomes. WASH services alone are often not sufficient and need to be combined with educational programmes. As pupils disseminate their acquired health-promoting knowledge to their (extended) families, improved WASH provisions and education in schools have beneficial effects also on the community. International organisations frequently roll out interventions in schools to improve WASH services and, in some cases, train pupils and teachers on safe WASH behaviours. How such interventions relate to local school education on WASH, health promotion and disease prevention knowledge, whether and how such knowledge and school books are integrated into WASH education interventions in schools, are knowledge gaps we fill. METHODS: We analyzed how Kenyan primary school science text book content supports WASH and health education by a book review including books used from class 1 through class 8, covering the age range from 6 to 13 years. We then conducted a rapid literature review of combined WASH interventions that included a behaviour change or educational component, and a rapid review of international policy guidance documents to contextualise the results and understand the relevance of books and school education for WASH interventions implemented by international organisations. We conducted a content analysis based on five identified thematic categories, including drinking water, sanitation, hygiene, environmental hygiene & health promotion and disease risks, and mapped over time the knowledge about WASH and disease prevention. RESULTS: The books comprehensively address drinking water issues, including sources, quality, treatment, safe storage and water conservation; risks and transmission pathways of various waterborne (Cholera, Typhoid fever), water-based (Bilharzia), vector-related (Malaria) and other communicable diseases (Tuberculosis); and the importance of environmental hygiene and health promotion. The content is broadly in line with internationally recommended WASH topics and learning objectives. Gaps remain on personal hygiene and handwashing, including menstrual hygiene, sanitation education, and related health risks and disease exposures. The depth of content varies greatly over time and across the different classes. Such locally available education materials already used in schools were considered by none of the WASH education interventions in the considered intervention studies. CONCLUSIONS: The thematic gaps/under-representations in books that we identified, namely sanitation, hygiene and menstrual hygiene education, are all high on the international WASH agenda, and need to be filled especially now, in the context of the current COVID-19 pandemic. Disconnects exist between school book knowledge and WASH education interventions, between policy and implementation, and between theory and practice, revealing missed opportunities for effective and sustainable behaviour change, and underlining the need for better integration. Considering existing local educational materials and knowledge may facilitate the buy-in and involvement of teachers and school managers in strengthening education and implementing improvements. We suggest opportunities for future research, behaviour change interventions and decision-making to improve WASH in schools.