Outbreak Size Research Articles

Potential reduction in transmission of COVID-19 by digital contact tracing systems: a modelling study.

Digital tools are being developed to support contact tracing as part of the global effort to control the spread of COVID-19. These include smartphone apps, Bluetooth-based proximity detection, location tracking and automatic exposure notification features. Evidence on the effectiveness of alternative approaches to digital contact tracing is so far limited. We use an age-structured branching process model of the transmission of COVID-19 in different settings to estimate the potential of manual contact tracing and digital tracing systems to help control the epidemic. We investigate the effect of the uptake rate and proportion of contacts recorded by the digital system on key model outputs: the effective reproduction number, the mean outbreak size after 30days and the probability of elimination. Effective manual contact tracing can reduce the effective reproduction number from 2.4 to around 1.5. The addition of a digital tracing system with a high uptake rate over 75% could further reduce the effective reproduction number to around 1.1. Fully automated digital tracing without manual contact tracing is predicted to be much less effective. For digital tracing systems to make a significant contribution to the control of COVID-19, they need be designed in close conjunction with public health agencies to support and complement manual contact tracing by trained professionals.

A study of SARS-COV-2 outbreaks in US federal prisons: the linkage between staff, incarcerated populations, and community transmission

BackgroundSince the novel coronavirus SARS-COV-2 was first identified to be circulating in the US on January 20, 2020, some of the worst outbreaks have occurred within state and federal prisons. The vulnerability of incarcerated populations, and the additional threats posed to the health of prison staff and the people they contact in surrounding communities underline the need to better understand the dynamics of transmission in the inter-linked incarcerated population/staff/community sub-populations to better inform optimal control of SARS-COV-2.MethodsWe examined SARS-CoV-2 case data from 101 non-administrative federal prisons between 5/18/2020 to 01/31/2021 and examined the per capita size of outbreaks in staff and the incarcerated population compared to outbreaks in the communities in the counties surrounding the prisons during the summer and winter waves of the SARS-COV-2 pandemic. We also examined the impact of decarceration on per capita rates in the staff/incarcerated/community populations.ResultsFor both the summer and winter waves we found significant inter-correlations between per capita rates in the outbreaks among the incarcerated population, staff, and the community.Over-all during the pandemic, per capita rates were significantly higher in the incarcerated population than in both the staff and community (paired Student’s t-test p = 0.03 and p < 0.001, respectively). Average per capita rates of incarcerated population outbreaks were significantly associated with prison security level, ranked from lowest per capita rate to highest: High, Minimum, Medium, and Low security.Federal prisons decreased the incarcerated population by a relative factor of 96% comparing the winter to summer wave (one SD range [90%,102%]). We found no significant impact of decarceration on per capita rates of SARS-COV-2 infection in the staff community populations, but decarceration was significantly associated with a decrease in incarcerated per capita rates during the winter wave (Negative Binomial regression p = 0.015).ConclusionsWe found significant evidence of community/staff/incarcerated population inter-linkage of SARS-COV-2 transmission. Further study is warranted to determine which control measures aimed at the incarcerated population and/or staff are most efficacious at preventing or controlling outbreaks.

Shared water facilities and risk of COVID-19 in resource-poor settings: A transmission modelling study

Shared water facilities are widespread in resource-poor settings within low- and middle-income countries. Since gathering water is essential, shared water sites may act as an important COVID-19 transmission pathway, despite stay-at-home recommendations. This analysis explores conditions under which shared water facility utilization may influence COVID-19 transmission. We developed two SEIR transmission models to explore COVID-19 dynamics. The first describes an urban setting, where multiple water sites are shared within a community, and the second describes a rural setting, where a single water site is shared among communities. We explored COVID-19 mitigation strategies including social distancing and adding additional water sites. Increased water site availability and social distancing independently attenuate attack rate and peak outbreak size through density reduction. In combination, these conditions result in interactive risk reductions. When water sharing intensity is high, risks are high regardless of the degree of social distancing. Even moderate reductions in water sharing can enhance the effectiveness of social distancing. In rural contexts, we observe similar but weaker effects. Enforced social distancing and density reduction at shared water sites can be an effective and relatively inexpensive mitigation effort to reduce the risk of COVID-19 transmission. Building additional water sites is more expensive but can increase the effectiveness of social distancing efforts at the water sites. As respiratory pathogen outbreaks—and potentially novel pandemics—will continue, infrastructure planning should consider the health benefits associated with respiratory transmission reduction when prioritizing investments.

Examination of the interaction between age-specific predation and chronic disease in the Greater Yellowstone Ecosystem.

Predators may create healthier prey populations by selectively removing diseased individuals. Predators typically prefer some ages of prey over others, which may, or may not, align with those prey ages that are most likely to be diseased. The interaction of age-specific infection and predation has not been previously explored and likely has sizable effects on disease dynamics. We hypothesize that predator cleansing effects will be greater when the disease and predation occur in the same prey age groups. We examine the predator cleansing effect using a model where both vulnerability to predators and pathogen prevalence vary with age. We tailor this model to chronic wasting disease (CWD) in mule deer and elk populations in the Greater Yellowstone Ecosystem, with empirical data from Yellowstone grey wolves and cougars. Model results suggest that under moderate, yet realistic, predation pressure from cougars and wolves independently, predators may decrease CWD outbreak size substantially and delay the accumulation of symptomatic deer and elk. The magnitude of this effect is driven by the ability of predators to selectively remove late-stage CWD infections that are likely the most responsible for transmission, but this may not be the age class they typically select. Thus, predators that select for infected young adults over uninfected juveniles have a stronger cleansing effect, and these effects are strengthened when transmission rates increase with increasing prey morbidity. There are also trade-offs from a management perspective-that is, increasing predator kill rates can result in opposing forces on prey abundance and CWD prevalence. Our modelling exploration shows that predators have the potential to reduce prevalence in prey populations when prey age and disease severity are considered, yet the strength of this effect is influenced by predators' selection for demography or body condition. Current CWD management focuses on increasing cervid hunting as the primary management tool, and our results suggest predators may also be a useful tool under certain conditions, but not necessarily without additional impacts on host abundance and demography. Protected areas with predator populations will play a large role in informing the debate over predator impacts on disease.

Modeling the early transmission of COVID-19 in New York and San Francisco using a pairwise network model.

Classical epidemiological models assume mass action. However, this assumption is violated when interactions are not random. With the recent COVID-19 pandemic, and resulting shelter in place social distancing directives, mass action models must be modified to account for limited social interactions. In this paper we apply a pairwise network model with moment closure to study the early transmission of COVID-19 in New York and San Francisco and to investigate the factors determining the severity and duration of outbreak in these two cities. In particular, we consider the role of population density, transmission rates and social distancing on the disease dynamics and outcomes. Sensitivity analysis shows that there is a strongly negative correlation between the clustering coefficient in the pairwise model and the basic reproduction number and the effective reproduction number. The shelter in place policy makes the clustering coefficient increase thereby reducing the basic reproduction number and the effective reproduction number. By switching population densities in New York and San Francisco we demonstrate how the outbreak would progress if New York had the same density as San Francisco and vice-versa. The results underscore the crucial role that population density has in the epidemic outcomes. We also show that under the assumption of no further changes in policy or transmission dynamics not lifting the shelter in place policy would have little effect on final outbreak size in New York, but would reduce the final size in San Francisco by 97%.

Quantifying transmissibility of SARS-CoV-2 and impact of intervention within long-term healthcare facilities.

Estimates of the basic reproduction number (R0) for COVID-19 are particularly variable in the context of transmission within locations such as long-term healthcare (LTHC) facilities. We sought to characterize the heterogeneity of R0 across known outbreaks within these facilities. We used a unique comprehensive dataset of all outbreaks that occurred within LTHC facilities in British Columbia, Canada as of 21 September 2020. We estimated R0 in 18 LTHC outbreaks with a novel Bayesian hierarchical dynamic model of susceptible, exposed, infected and recovered individuals, incorporating heterogeneity of R0 between facilities. We further compared these estimates to those obtained with standard methods that use the exponential growth rate and maximum likelihood. The total size of outbreaks varied dramatically, with range of attack rates 2%–86%. The Bayesian analysis provided an overall estimate of R0 = 2.51 (90% credible interval 0.47–9.0), with individual facility estimates ranging between 0.56 and 9.17. Uncertainty in these estimates was more constrained than standard methods, particularly for smaller outbreaks informed by the population-level model. We further estimated that intervention led to 61% (52%–69%) of all potential cases being averted within the LTHC facilities, or 75% (68%–79%) when using a model with multi-level intervention effect. Understanding of transmission risks and impact of intervention are essential in planning during the ongoing global pandemic, particularly in high-risk environments such as LTHC facilities.

The co-circulating transmission dynamics of SARS-CoV-2 Alpha and Eta variants in Nigeria: A retrospective modeling study of COVID-19.

BackgroundThe COVID-19 pandemic poses serious threats to public health globally, and the emerging mutations in SARS-CoV-2 genomes has become one of the major challenges of disease control. In the second epidemic wave in Nigeria, the roles of co-circulating SARS-CoV-2 Alpha (ie, B.1.1.7) and Eta (ie, B.1.525) variants in contributing to the epidemiological outcomes were of public health concerns for investigation.MethodsWe developed a mathematical model to capture the transmission dynamics of different types of strains in Nigeria. By fitting to the national-wide COVID-19 surveillance data, the transmission advantages of SARS-CoV-2 variants were estimated by likelihood-based inference framework.ResultsThe reproduction numbers were estimated to decrease steadily from 1.5 to 0.8 in the second epidemic wave. In December 2020, when both Alpha and Eta variants were at low prevalent levels, their transmission advantages (against the wild type) were estimated at 1.51 (95% credible intervals (CrI) = 1.48, 1.54), and 1.56 (95% CrI = 1.54, 1.59), respectively. In January 2021, when the original variants almost vanished, we estimated a weak but significant transmission advantage of Eta against Alpha variants with 1.14 (95% CrI = 1.11, 1.16).ConclusionsOur findings suggested evidence of the transmission advantages for both Alpha and Eta variants, of which Eta appeared slightly more infectious than Alpha. We highlighted the critical importance of COVID-19 control measures in mitigating the outbreak size and relaxing the burdens to health care systems in Nigeria.

Case-area targeted preventive interventions to interrupt cholera transmission: Current implementation practices and lessons learned.

Cholera is a major cause of mortality and morbidity in low-resource and humanitarian settings. It is transmitted by fecal-oral route, and the infection risk is higher to those living in and near cholera cases. Rapid identification of cholera cases and implementation of measures to prevent subsequent transmission around cases may be an efficient strategy to reduce the size and scale of cholera outbreaks. We investigated implementation of cholera case-area targeted interventions (CATIs) using systematic reviews and case studies. We identified 11 peer-reviewed and eight grey literature articles documenting CATIs and completed 30 key informant interviews in case studies in Democratic Republic of Congo, Haiti, Yemen, and Zimbabwe. We documented 15 outbreaks in 12 countries where CATIs were used. The team composition and the interventions varied, with water, sanitation, and hygiene interventions implemented more commonly than those of health. Alert systems triggering interventions were diverse ranging from suspected cholera cases to culture confirmed cases. Selection of high-risk households around the case household was inconsistent and ranged from only one case to approximately 100 surrounding households with different methods of selecting them. Coordination among actors and integration between sectors were consistently reported as challenging. Delays in sharing case information impeded rapid implementation of this approach, while evaluation of the effectiveness of interventions varied. CATIs appear effective in reducing cholera outbreaks, but there is limited and context specific evidence of their effectiveness in reducing the incidence of cholera cases and lack of guidance for their consistent implementation. We propose to 1) use uniform cholera case definitions considering a local capacity to trigger alert; 2) evaluate the effectiveness of individual or sets of interventions to interrupt cholera, and establish a set of evidence-based interventions; 3) establish criteria to select high-risk households; and 4) improve coordination and data sharing amongst actors and facilitate integration among sectors to strengthen CATI approaches in cholera outbreaks.

An analysis of COVID-19 outbreaks in schools in South and Southwestern United States using open-source data

The reopening of schools for the 2020 fall academic session in the United States was linked to many cases of COVID-19 infection. This analysis utilized a dataset of school-related outbreaks created by National Education Association (NEA) to study attack rates and COVID-19 trends in schools located in the south and southwestern regions of the United States. The highest attack rate was observed in Mississippi for both students and teachers. In most states, the attack rate was higher for students in high schools than elementary schools, whereas the reverse was true for teachers. Most outbreaks had fewer than five cases, with only a small number of outbreaks that involved more than 10 cases in the duration under study. Outbreak size was found to be the highest in the state of Texas. Epidemic curves of new cases in most of these states showed features of a propagated outbreak. In some states, a rise in school-related cases coincided with rise in cases in the state. Findings related to size and pattern of school outbreaks may have significant implications for defining public health policies related to school functioning during infectious disease outbreaks.

Spatial Clustering of Vaccine Exemptions on the Risk of a Measles Outbreak.

Areas of increased school-entry vaccination exemptions play a key role in epidemics of vaccine-preventable diseases in the United States. California eliminated nonmedical exemptions in 2016, which increased overall vaccine coverage but also rates of medical exemptions. We examine how spatial clustering of exemptions contributed to measles outbreak potential pre- and postpolicy change. We modeled measles transmission in an empirically calibrated hypothetical population of youth aged 0 to 17 years in California and compared outbreak sizes under the observed spatial clustering of exemptions in schools pre- and postpolicy change with counterfactual scenarios of no postpolicy change increase in medical exemptions, no clustering of exemptions, and lower population immunization levels. The elimination of nonmedical exemptions significantly reduced both average and maximal outbreak sizes, although increases in medical exemptions resulted in more than twice as many infections, on average, than if medical exemptions were maintained at prepolicy change levels. Spatial clustering of nonmedical exemptions provided some initial protection against random introduction of measles infections; however, it ultimately allowed outbreaks with thousands more infections than when exemptions were randomly distributed. The large-scale outbreaks produced by exemption clusters could not be reproduced when exemptions were distributed randomly until population vaccination was lowered by >6 percentage points. Despite the high overall vaccinate rate, the spatial clustering of exemptions in schools was sufficient to threaten local herd immunity and reduce protection from measles outbreaks. Policies strengthening vaccine requirements may be less effective if alternative forms of exemptions (eg, medical) are concentrated in existing low-immunization areas.

Long-lasting consequences of coronavirus disease 19 pneumonia: a systematic review.

Coronavirus disease 19 (COVID-19) is an infectious disease caused by the newly discovered severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We have plenty of data about the clinical features of the disease's acute phase, while little is known about the long-term consequences on survivors. We aimed to review systematically emerging evidence about clinical and functional consequences of COVID-19 pneumonia months after hospital discharge. Current evidence supports the idea that a high proportion of COVID-19 survivors complain of symptoms months after the acute illness phase, being fatigue and reduced tolerance to physical effort the most frequently reported symptom. The strongest association for these symptoms is with the female gender, while disease severity seems less relevant. Respiratory symptoms are associated with a decline in respiratory function and, conversely, seem to be more frequent in those who experienced a more severe acute pneumonia. Current evidence highlighted a persistent motor impairment which is, again, more prevalent among those survivors who experienced a more severe acute phase of the disease. Additionally, the persistence of symptoms is a primary determinant of mental health outcome, with anxiety, depression, sleep disturbances, and post-traumatic stress symptoms being commonly reported in COVID-19 survivors. Current literature highlights the importance of a multidisciplinary approach to Coronavirus Disease 19 since the sequelae appear to involve different organs and systems. Given the pandemic outbreak's size, this is a critical public health issue: a better insight on this topic should inform clinical decisions about the modalities of follow-up for COVID-19 survivors.

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.

Maximizing spreading in complex networks with risk in node activation

It is widely acknowledged that the initial spreaders play an important role in the spread of information in complex networks. Thus, a variety of centrality-based methods have been proposed for identifying the most influential spreaders. However, most existing studies overlook the fact that, in real social networks, it is more costly and difficult to convince influential individuals to act as initial spreaders, resulting in a high risk to maximal spreading. In this paper, we address this problem on the basis of the assumption that the activation of large-degree nodes carries a higher risk than that of small-degree nodes. We aim to identify the initial spreaders that most effectively maximize the spreading when considering both the activation risk and the outbreak size of the initial spreaders. Analysis of random networks reveals that the degree of the optimal initial spreaders does not correspond to the largest node degree in the network, but is instead determined by the infection probability and difference in activation risk among nodes with different degrees. We propose a risk-aware metric to identify the most effective spreaders in real networks. Numerical simulations show that this risk-aware metric outperforms the existing benchmark centralities in terms of maximizing the spreading.

Mathematical modeling of bird flu with vaccination and treatment for the poultry farms

A deterministic six-compartmental model was developed based on the progression of the disease in poultry, the epidemiological status of the individuals, and intervention measures. The Runge-Kutta method is applied to calculate the variables of the system of equations of the proposed model. The evolution of the epidemic provides some results, such as reproduction number, vaccine efficiency, and antiviral treatment. Numerical results show that the outbreak sizes known as the infected curves increase and decrease with the vaccine limitation rate and treatment rate, respectively, for a specific transmission rate. The calculated results of the reproduction number indicate that avian influenza would spread when vaccine efficiency is less than 70%, and the primary reproduction number is greater than 1. Finally, the disease-free equilibrium of the model is found locally and globally asymptotically stable for R0 < 1.

Risk factors and spatio-temporal patterns of livestock anthrax in Khuvsgul Province, Mongolia.

Anthrax is a worldwide zoonotic disease. Anthrax has long been a public health and socio-economic issue in Mongolia. Presently, there is no spatial information on carcass burial sites as a potential hazard of future anthrax outbreaks and possible risk factors associated with anthrax occurrences in Mongolia. Here, we analyze retrospective data (1986–2015) on the disposal sites of livestock carcasses to describe historical spatio-temporal patterns of livestock anthrax in Khuvsgul Province, which showed the highest anthrax incidence rate in Mongolia. From the results of spatial mean and standard deviational ellipse analyses, we found that the anthrax spatial distribution in livestock did not change over the study period, indicating a localized source of exposure. The multi-distance spatial cluster analysis showed that carcass sites distributed in the study area are clustered. Using kernel density estimation analysis on carcass sites, we identified two anthrax hotspots in low-lying areas around the south and north regions. Notably, this study disclosed a new hotspot in the northern part that emerged in the last decade of the 30-year study period. The highest proportion of cases was recorded in cattle, whose prevalence per area was highest in six districts (i.e., Murun, Chandmani-Undur, Khatgal, Ikh-Uul, Tosontsengel, and Tsagaan-Uul), suggesting that vaccination should prioritize cattle in these districts. Furthermore, size of outbreaks was influenced by the annual summer mean air temperature of Khuvsgul Province, probably by affecting the permafrost freeze-thawing activity.

Modeling the impacts of contact tracing on an epidemic with asymptomatic infection

Contact tracing is widely adopted to inhibit the epidemics in the global world, and has been proved to be very effective in reducing infections. But it is rarely investigated whether contact tracing can completely eradicate an epidemic with asymptomatic infection. This paper proposes a novel model to explore the impacts of contact tracing on the outbreak size and outbreak threshold of an epidemic with asymptomatic infection in a two-layered network structure. Based on contact tracing, three types of effects are considered simultaneously: social distancing on the close contacts, infection test on the close contacts, and arousing risk perception from the close contacts. The results indicate that contact tracing and its three effects can largely reduce the number of infections. Among the three effects, the effect of social distancing is more effective because it acts on both the susceptible nodes and the asymptomatic nodes, while the other two only act on one type of nodes. However, contact tracing and its three effects are unable to change the epidemic threshold, even if the asymptomatic nodes and symptomatic nodes are all set to be infectious. The primary reason is that the identification of close contacts is driven by the detection of infections, and is lagged behind the outbreak of epidemic. In fact, the threshold for close contacts to emerge is highly dependent on the epidemic threshold. When the epidemic size approaches 0, close contacts will also disappear. To increase the efficiency of contact tracing on the epidemic threshold, the optimal strategy should directly target the normal people besides the close-contact individuals.

Modelling response strategies for controlling gonorrhoea outbreaks in men who have sex with men in Australia.

The ability to treat gonorrhoea with current first-line drugs is threatened by the global spread of extensively drug resistant (XDR) Neisseria gonorrhoeae (NG) strains. In Australia, urban transmission is high among men who have sex with men (MSM) and importation of an XDR NG strain in this population could result in an epidemic that would be difficult and costly to control. An individual-based, anatomical site-specific mathematical model of NG transmission among Australian MSM was developed and used to evaluate the potential for elimination of an imported NG strain under a range of case-based and population-based test-and-treat strategies. When initiated upon detection of the imported strain, these strategies enhance the probability of elimination and reduce the outbreak size compared with current practice (current testing levels and no contact tracing). The most effective strategies combine testing targeted at regular and casual partners with increased rates of population testing. However, even with the most effective strategies, outbreaks can persist for up to 2 years post-detection. Our simulations suggest that local elimination of imported NG strains can be achieved with high probability using combined case-based and population-based test-and-treat strategies. These strategies may be an effective means of preserving current treatments in the event of wider XDR NG emergence.

Epidemic Spreading in Trajectory Networks

Epidemics of infectious diseases, such as the one caused by the rapid spread of the coronavirus disease 2019 (COVID-19), have tested the world's more advanced health systems and have caused an enormous societal and economic damage. The mechanism of contagion is well understood. As people move around, over time, they regularly engage in social interactions. The spatiotemporal network representing these interactions constitutes the backbone on which an epidemic spreads, causing outbreaks. At the same time, advanced technological responses have claimed some success in controlling the epidemic based on digital contact tracing technologies. Motivated by these observations, we design, develop and evaluate a stochastic agent-basedmodel of epidemic spreading in spatiotemporal networks informed by mobility data of individuals (trajectories). The model focuses on individual variation in mobility patterns that affects the degree of exposure to the disease. Understanding the role that individual nodes play in the process of disease spreading through network effects is fundamental as it allows to (i) assess the risk of infection of individuals, (ii) assess the size of a disease outbreak due to specific individuals, and (iii) assess targeted intervention strategies that aim to control the epidemic spreading. We perform a comprehensive analysis of the model employing COVID-19 as a use case. The results indicate that simple individual-based intervention strategies that exhibit significant network effects can effectively control the spread of an epidemic. We have also demonstrated that targeted interventions can outperform generic intervention strategies. Overall, our work provides an evidence-based data-driven model to support decision making and inform public policy regarding intervention strategies for containing or mitigating the epidemic spread.

Impact of COVID-19 epidemic on Lombardy’s prisons (Italy): transmission and measure of prevention

Abstract Background Early on in the COVID-19 pandemic, scientific community highlighted the potential risk of epidemics occurring inside prisons. Consequently, specific operational guidelines were promptly released and stringent containment measures quickly implemented, including access restriction to essential staff only. During the 2nd epidemic wave prevention strategies were gradually relaxed, visitors were readmitted and people in prison (PiP) activities were repristinated. Here, we describe the spread of COVID-19 in 18 detention facilities in Lombardy region, Italy, during the 1st(Mar-Jul 2020) and 2nd(Oct 2020-Feb 2021) waves and the impact of prevention and control measures implemented in prison. Methods We conducted a descriptive retrospective analysis of cases' distribution. Moreover, a comparison among the epidemic burden affecting POs and PiP and a correlation analysis between the number of cases occurred and prevention measures implemented were carried out. Results Our population consists of a monthly average of 2269 prison officers (POs) and 7604 PiP. POs were at higher risk of contracting COVID-19 during both waves. Occurrence of cases among PiP was temporally associated with cases among POs. There was heterogeneity in the size of outbreaks across different prison facilities. During the second wave, COVID-19 spread more widely in prison both among POs (2ndwave attack rate (AR) per 1000 individuals: 27.2; monthly range (MR): 0.9-64.5 vs 1stwave AR 2.4; MR: 0.0-12.1) and PiP (2ndwave AR: 23.5; MR: 11.6-50.8 vs 1stwave AR 1.6; MR: 0-5.2). Conclusions Prevention and control measures adopted timely during the first wave were effective to limit Sars-CoV-2 spread within prison facilities. According to our findings, POs and PiP are population groups at high risk for acquiring and transmitting COVID-19 and should be prioritized for testing, active case finding and vaccination. This study highlights the importance of including prison settings within emergency preparedness plans. Key messages Stringent prevention and control measures when adopted timely were effective to protect detention facilities from Sars-CoV-2 spread. Our study, reporting the burden of COVID-19 epidemic experienced by prison of Lombardy, Italy, highlights the importance of including prison settings within emergency preparedness plans.

Timely surveillance and temporal calibration of disease response against human infectious diseases

BackgroundDisease surveillance and response are critical components of epidemic preparedness. The disease response, in most cases, is a set of reactive measures that follow the outcomes of the disease surveillance. Hence, timely surveillance is a prerequisite for an effective response.Methodology/principal findingsWe apply epidemiological soundness criteria in combination with the Latent Influence Point Process and time-to-event models to construct a disease spread network. The network implicitly quantifies the fertility (whether a case leads to secondary cases) and reproduction (number of secondary cases per infectious case) of the cases as well as the size and generations (of the infection chain) of the outbreaks. We test our approach by applying it to historic dengue case data from Australia. Using the data, we empirically confirm that high morbidity relates positively with delay in disease response. Moreover, we identify what constitutes timely surveillance by applying various thresholds of disease response delay to the network and report their impact on case fertility, reproduction, number of generations and ultimately, outbreak size. We observe that enforcing a response delay threshold of 5 days leads to a large average reduction across all parameters (occurrence 87%, reproduction 83%, outbreak size 80% and outbreak generations 47%), whereas extending the threshold to 10 days, in comparison, significantly limits the effectiveness of the response actions. Lastly, we identify the components of the disease surveillance system that can be calibrated to achieve the identified thresholds.ConclusionWe identify practically achievable, timely surveillance thresholds (on temporal scale) that lead to an effective response and identify how they can be satisfied. Our approach can be utilized to provide guidelines on spatially and demographically targeted resource allocation for public awareness campaigns as well as to improve diagnostic abilities and turn-around times for the doctors and laboratories involved.