Quantifying the impact of individual and collective compliance with infection control measures for ethical public health policy

Compliance with infectious disease control measures can benefit public health but be burdensome for individuals. This raises ethical questions regarding the value of the public health benefit created by individual and collective compliance. Answering such questions requires estimating the total benefit from an individual’s compliance, and how much of that benefit is experienced by others. This is complicated by “overdetermination” in infectious disease transmission: each susceptible person may have contact with more than one infectious individual, such that preventing one transmission may have no net effect if the same susceptible person is infected later. This article explores mathematical techniques enabling quantification of the impacts of individuals and groups complying with three types of public health measures: quarantine of arrivals, isolation of infected individuals, and vaccination/prophylaxis. The models presented suggest that these interventions all exhibit synergy: each intervention becomes more effective on a per-individual basis as the number complying increases, because overdetermination of outcomes is reduced, Thus additional compliance reduces transmission to a greater degree.

Scale-free dynamics of Covid-19 in a Brazilian city

Mathematical models can provide insights into the control of pandemic COVID-19, which remains a global priority. The dynamics of directly-transmitted infectious diseases, such as COVID-19, are usually described by compartmental models where individuals are classified as susceptible, infected and removed. These SIR models typically assume homogenous transmission of infection, even in large populations, a simplification that is convenient but inconsistent with observations. Here we use original data on the dynamics of COVID-19 spread in a Brazilian city to investigate the structure of the transmission network. We find that transmission can be described by a network in which each infectious individual has a small number of susceptible contacts, of the order of 2-5, which is independent of total population size. Compared with standard models of homogenous mixing, this scale-free, fractal infection process gives a better description of COVID-19 dynamics through time. In addition, the contact process explains the geographically localized clusters of disease seen in this Brazilian city. Our scale-free model can help refine criteria for physical and social

SARS-CoV-2 aerosol transmission in schools: the effectiveness of different interventions

Background: Indoor aerosol transmission of SARS-CoV-2 has been widely recognized, especially in schools where children remain in close proximity and largely unvaccinated. Measures such as strategic natural ventilation and high efficiency particulate air (HEPA) filtration remain poorly implemented and mask mandates are often progressively lifted as vaccination rollout is enhanced. Methods: We adapted a previously developed aerosol transmission model to study the effect of interventions (natural ventilation, face masks, HEPA filtration, and their combinations) on the concentration of virus particles in a classroom of 160 m3 containing one infectious individual. The cumulative dose of viruses absorbed by exposed occupants was calculated. Results: The most effective single intervention was natural ventilation through the full opening of six windows all day during the winter (14-fold decrease in cumulative dose), followed by the universal use of surgical face masks (8-fold decrease). In the spring/summer, natural ventilation was only effective (≥ 2-fold decrease) when windows were fully open all day. In the winter, partly opening two windows all day or fully opening six windows at the end of each class was effective as well (≥ 2-fold decrease). Opening windows during yard and lunch breaks only had minimal effect (≤ 1.2-fold decrease). One HEPA filter was as effective as two windows partly open all day during the winter (2.5-fold decrease) while two filters were more effective (4-fold decrease). Combined interventions (i.e., natural ventilation, masks, and HEPA filtration) were the most effective (≥ 30-fold decrease). Combined interventions remained highly effective in the presence of a super-spreader. Conclusions: Natural ventilation, face masks, and HEPA filtration are effective interventions to reduce SARS-CoV-2 aerosol transmission. These measures should be combined and complemented by additional interventions (e.g., physical distancing, hygiene, testing, contact tracing, and vaccination) to maximize benefit.

The impact of dengue illness on social distancing and caregiving behavior

Background Human mobility among residential locations can drive dengue virus (DENV) transmission dynamics. Recently, it was shown that individuals with symptomatic DENV infection exhibit significant changes in their mobility patterns, spending more time at home during illness. This change in mobility is predicted to increase the risk of acquiring infection for those living with or visiting the ill individual. It has yet to be considered, however, whether social contacts are also changing their mobility, either by socially distancing themselves from the infectious individual or increasing contact to help care for them. Social, or physical, distancing and caregiving could have diverse yet important impacts on DENV transmission dynamics; therefore, it is necessary to better understand the nature and frequency of these behaviors including their effect on mobility. Methodology and principal findings Through community-based febrile illness surveillance and RT-PCR infection confirmation, 67 DENV positive (DENV+) residents were identified in the city of Iquitos, Peru. Using retrospective interviews, data were collected on visitors and home-based care received during the illness. While 15% of participants lost visitors during their illness, 22% gained visitors; overall, 32% of all individuals (particularly females) received visitors while symptomatic. Caregiving was common (90%), particularly caring by housemates (91%) and caring for children (98%). Twenty-eight percent of caregivers changed their behavior enough to have their work (and, likely, mobility patterns) affected. This was significantly more likely when caring for individuals with low “health-related quality of well-being” during illness (Fisher’s Exact, p = 0.01). Conclusions/Significance Our study demonstrates that social contacts of individuals with dengue modify their patterns of visitation and caregiving. The observed mobility changes could impact a susceptible individual’s exposure to virus or a presymptomatic/clinically inapparent individual’s contribution to onward transmission. Accounting for changes in social contact mobility is imperative in order to get a more accurate understanding of DENV transmission.

Quantifying superspreading for COVID-19 using Poisson mixture distributions

The number of secondary cases, i.e. the number of new infections generated by an infectious individual, is an important parameter for the control of infectious diseases. When individual variation in disease transmission is present, like for COVID-19, the distribution of the number of secondary cases is skewed and often modeled using a negative binomial distribution. However, this may not always be the best distribution to describe the underlying transmission process. We propose the use of three other offspring distributions to quantify heterogeneity in transmission, and we assess the possible bias in estimates of the mean and variance of this distribution when the data generating distribution is different from the one used for inference. We also analyze COVID-19 data from Hong Kong, India, and Rwanda, and quantify the proportion of cases responsible for 80% of transmission, $$p_{80\%}$$ p 80 % , while acknowledging the variation arising from the assumed offspring distribution. In a simulation study, we find that variance estimates may be biased when there is a substantial amount of heterogeneity, and that selection of the most accurate distribution from a set of distributions is important. In addition we find that the number of secondary cases for two of the three COVID-19 datasets is better described by a Poisson-lognormal distribution.

How did governmental interventions affect the spread of COVID-19 in European countries?

Background To reduce the transmission of the severe acute respiratory syndrome coronavirus 2 in its first wave, European governments have implemented successive measures to encourage social distancing. However, it remained unclear how effectively measures reduced the spread of the virus. We examined how the effective-contact rate (ECR), the mean number of daily contacts for an infectious individual to transmit the virus, among European citizens evolved during this wave over the period with implemented measures, disregarding a priori information on governmental measures. Methods We developed a data-oriented approach that is based on an extended Susceptible-Exposed-Infectious-Removed (SEIR) model. Using the available data on the confirmed numbers of infections and hospitalizations, we first estimated the daily total number of infectious-, exposed- and susceptible individuals and subsequently estimated the ECR with an iterative Poisson regression model. We then compared change points in the daily ECRs to the moments of the governmental measures. Results The change points in the daily ECRs were found to align with the implementation of governmental interventions. At the end of the considered time-window, we found similar ECRs for Italy (0.29), Spain (0.24), and Germany (0.27), while the ECR in the Netherlands (0.34), Belgium (0.35) and the UK (0.37) were somewhat higher. The highest ECR was found for Sweden (0.45). Conclusions There seemed to be an immediate effect of banning events and closing schools, typically among the first measures taken by the governments. The effect of additionally closing bars and restaurants seemed limited. For most countries a somewhat delayed effect of the full lockdown was observed, and the ECR after a full lockdown was not necessarily lower than an ECR after (only) a gathering ban.

Stability Analysis of Mathematical Model of a Relapse Tuberculosis Incorporating Vaccination

A general SIQRM epidemic model with vaccination and relapse possibility is proposed for analysis in this work. The idea behind the proposed model is to check the effect of immunity obtained from vaccine or treatment, quarantine effect as well as waning effect of immunity on the transmission rate of Tuberculosis within a population that is subjected to proper education without restricted access. Some other infectious diseases in this category include measles and Ebola. Two equilibrium states of the proposed model are obtained as well as the effective reproduction number(Reff). Stability analysis of the model at the Infection Free Equilibrium(I.F.E) state is established on the condition that Reff<1. Numerical simulation for the general SIQRM model was done using specific data for Tuberculosis disease and the result shows that proper education, vaccination and early diagnosis of an infectious individual for quarantining is an efficient way by which the spread of Tuberculosis can be reduced in the population while adequate medical attention yield better result for detected cases

Early detection of SARS-CoV-2 without human intervention to combat COVID-19 using drone technology

Purpose The focus of the current study to combat the ongoing pandemic by preventing the transmission using the Unmanned aerial vehicle system. The transmission of the COVID-19 pandemic can be avoided only by finding the infectious person at the right time. Despite the thermal scanning camera and artificial intelligence technology, finding the infectious individual at many occasions has become questionable. Design/methodology/approach The drones are equipped with the thermal vision camera to detect the human body temperature. In addition, they are equipped with the disinfect tank to sanitize the indoor and outdoor environments based on the requirement. Findings Once the lockdown eased, the experts fear that the infection rate can increase in the high-density population countries such as India. The drone with thermal screening and day vision camera can detect the infection of the person without any human intervention. Further, they can also be used to disinfect the public places by aerial spraying. Practical implications Using the drones to monitor the work places, shopping mall and education institution to identify the mask through artificial intelligence is viable without human intervention in short span of time. Originality/value COVID-19 impact on the global was awful. Finding a suitable technology to combat the COVID-19 is much necessary. This conceptual study proposed to use drone technology to identify the infection at right time even on densely populated streets. Further, artificial technology can be used to detect the person who was not wearing mask. Added to above, disinfect tank can be mounted to sanitize the area in the required places.

Event-specific interventions to minimize COVID-19 transmission

COVID-19 is a global pandemic with over 25 million cases worldwide. Currently, treatments are limited, and there is no approved vaccine. Interventions such as handwashing, masks, social distancing, and “social bubbles” are used to limit community transmission, but it is challenging to choose the best interventions for a given activity. Here, we provide a quantitative framework to determine which interventions are likely to have the most impact in which settings. We introduce the concept of “event R,” the expected number of new infections due to the presence of a single infectious individual at an event. We obtain a fundamental relationship between event R and four parameters: transmission intensity, duration of exposure, the proximity of individuals, and the degree of mixing. We use reports of small outbreaks to establish event R and transmission intensity in a range of settings. We identify principles that guide whether physical distancing, masks and other barriers to transmission, or social bubbles will be most effective. We outline how this information can be obtained and used to reopen economies with principled measures to reduce COVID-19 transmission.