Vaccination for communicable endemic diseases: optimal allocation of initial and booster vaccine doses.

For some communicable endemic diseases (e.g., influenza, COVID-19), vaccination is an effective means of preventing the spread of infection and reducing mortality, but must be augmented over time with vaccine booster doses. We consider the problem of optimally allocating a limited supply of vaccines over time between different subgroups of a population and between initial versus booster vaccine doses, allowing for multiple booster doses. We first consider an SIS model with interacting population groups and four different objectives: those of minimizing cumulative infections, deaths, life years lost, or quality-adjusted life years lost due to death. We solve the problem sequentially: for each time period, we approximate the system dynamics using Taylor series expansions, and reduce the problem to a piecewise linear convex optimization problem for which we derive intuitive closed-form solutions. We then extend the analysis to the case of an SEIS model. In both cases vaccines are allocated to groups based on their priority order until the vaccine supply is exhausted. Numerical simulations show that our analytical solutions achieve results that are close to optimal with objective function values significantly better than would be obtained using simple allocation rules such as allocation proportional to population group size. In addition to being accurate and interpretable, the solutions are easy to implement in practice. Interpretable models are particularly important in public health decision making.

Sociodemographic Variables Can Guide Prioritized Testing Strategies for Epidemic Control in Resource-Limited Contexts.

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. However, it remains unclear how to implement such surveillance and control when network data are unavailable. METHODS: We evaluated the ability of sociodemographic proxies of degree centrality to guide prioritized testing of infected individuals compared to known degree centrality. Proxies were estimated via readily available sociodemographic variables (age, gender, marital status, educational attainment, household size). We simulated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemics via a susceptible-exposed-infected-recovered individual-based model on 2 contact networks from rural Madagascar to test applicability of these findings to low-resource contexts. RESULTS: Targeted testing using sociodemographic proxies performed similarly to targeted testing using known degree centralities. At low testing capacity, using proxies reduced 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 sociodemographic proxies when social network data are unavailable.

[The history of the fight against cholera epidemics in the Krasnodar territory in 1892 and 1910 (based on archival data)].

Epidemics of a wide variety of infectious diseases were constantly recorded in Russia. Asian cholera occupied a special place among epidemic diseases. In 1892, cholera was imported into the Russian Empire through the Black Sea ports, which reached the territory of the Kuban region by the summer of the same year. In 1892, about 300 thousand people died of cholera in Russia. They still did not know how to treat this terrible disease, did not know its mechanism of spread, as well as the peculiarities of its course. The article, prepared on the basis of an analysis of documentary data from the Archive Department of the administration of the municipal formation of the city of Novorossiysk and the archival Department of the Administration of the municipal formation of the Mostovsky district, examines the cholera epidemic that swept the territories of the Kuban region in 1892 and 1910. The authors characterize the main factors that contributed to the rapid spread of infection and assess the measures that were taken to combat the disease: the implementation of anti-cholera measures organized by the authorities was greatly hampered by the lack of education, prejudices and superstitions of the vast majority of the population; representatives of the nonresident population not only did not comply with basic standards of personal hygiene, but also expressed distrust, and sometimes and hostility towards doctors. Using archival data, the authors investigate the impact of infectious diseases on the demographic indicators of these settlements.

[Substantive approach to ensuring infectious safety in forensic medical expert practice]. / Predmetnyi podkhod k obespecheniyu infektsionnoi bezopasnosti v sudebno-meditsinskoi ekspertnoi deyatel'nosti.

The objective of the study is to analyze existing measures and identify problematic aspects of ensuring the organization of infectious safety activity in forensic medical expert institutions, to develop ways of their solution. Documents regulating the functional principles of forensic medical expert institutions in cases of detection or suspicion of infectious pathology have been examined. Specific proposals for the development of a system to ensure infectious safety in the organization of work in the bureau of forensic medical expertise have been formulated.

An adaptive testing strategy for efficient utilization of healthcare resources during an epidemic.

Lockdowns are found to be effective against rapidly spreading epidemics like COVID-19. Two downsides to strategies rooted in social distancing and lockdowns are that they adversely affect the economy and prolong the duration of the epidemic. The extended duration observed in these strategies is often due to the under-utilization of medical facilities. Even though an under-utilized health care system is preferred over an overwhelmed one, an alternate strategy could be to maintain medical facilities close to their capacity, with a factor of safety. We explore the practicality of this alternate mitigation strategy and show that it can be achieved by varying the testing rate. We present an algorithm to calculate the number of tests per day to maintain medical facilities close to their capacity. We illustrate the efficacy of our strategy by showing that it reduced the epidemic duration by 40% in comparison to lockdown-based strategies.

A global study of screening intensity and economic status on epidemic control performance during various epidemic periods of COVID-19 mutant strains.

This study analyzed global data on epidemic control measures and economic conditions in different countries during different mutant strain epidemic periods, including the Alpha, Delta, and Omicron strains. The study estimated the elasticity coefficient through a log-log model, which represents the percent change of the confirmed case number with respect to a percent change in the total number of screening tests in a country for epidemic control. The 7-day rolling data of screening tests and confirmed cases from the Our World in Data database for the pandemic periods of Alpha strain in 2020, Delta strain in 2021, and Omicron strain in 2022, suggest that the magnitude of the elasticity was associated with the economic condition of a country. Compared with the results during either Alpha or Delta pandemic period, the Omicron pandemic has a much higher estimated elasticity coefficient of 1.317 (Alpha: 0.827 and Delta: 0.885). Further examining economic conditions categorized by quartile ranges, the results indicate that the elasticity is statistically significantly lower in countries with gross domestic product (GDP) per capita between $11,354 and $26,651, and in countries with GDP per capita above $26,651 than in countries with GDP per capita below $3,335. These results suggest that countries should consider not only epidemiological measures but also economic conditions when formulating epidemic control strategies. This study highlights the importance of assessing the appropriateness of epidemic control strategies within a country and provides valuable insights into the effectiveness of such strategies, particularly in the context of community screening.

HRL4EC: Hierarchical reinforcement learning for multi-mode epidemic control.

Infectious diseases, such as Black Death, Spanish Flu, and COVID-19, have accompanied human history and threatened public health, resulting in enormous infections and even deaths among citizens. Because of their rapid development and huge impact, laying out interventions becomes one of the most critical paths for policymakers to respond to the epidemic. However, the existing studies mainly focus on epidemic control with a single intervention, which makes the epidemic control effectiveness severely compromised. In view of this, we propose a Hierarchical Reinforcement Learning decision framework for multi-mode Epidemic Control with multiple interventions called HRL4EC. We devise an epidemiological model, referred to as MID-SEIR, to describe multiple interventions' impact on transmission explicitly, and use it as the environment for HRL4EC. Besides, to address the complexity introduced by multiple interventions, this work transforms the multi-mode intervention decision problem into a multi-level control problem, and employs hierarchical reinforcement learning to find the optimal strategies. Finally, extensive experiments are conducted with real and simulated epidemic data to validate the effectiveness of our proposed method. We further analyze the experiment data in-depth, conclude a series of findings on epidemic intervention strategies, and make a visualization accordingly, which can provide heuristic support for policymakers' pandemic response.

3D face recognition algorithm based on deep Laplacian pyramid under the normalization of epidemic control.

Under the normalization of epidemic control in COVID-19, it is essential to realize fast and high-precision face recognition without feeling for epidemic prevention and control. This paper proposes an innovative Laplacian pyramid algorithm for deep 3D face recognition, which can be used in public. Through multi-mode fusion, dense 3D alignment and multi-scale residual fusion are ensured. Firstly, the 2D to 3D structure representation method is used to fully correlate the information of crucial points, and dense alignment modeling is carried out. Then, based on the 3D critical point model, a five-layer Laplacian depth network is constructed. High-precision recognition can be achieved by multi-scale and multi-modal mapping and reconstruction of 3D face depth images. Finally, in the training process, the multi-scale residual weight is embedded into the loss function to improve the network's performance. In addition, to achieve high real-time performance, our network is designed in an end-to-end cascade. While ensuring the accuracy of identification, it guarantees personnel screening under the normalization of epidemic control. This ensures fast and high-precision face recognition and establishes a 3D face database. This method is adaptable and robust in harsh, low light, and noise environments. Moreover, it can complete face reconstruction and recognize various skin colors and postures.

Structure, functions, performance and gaps of event-based surveillance (EBS) in Sudan, 2021: a cross-sectional review.

BACKGROUND: Event-based surveillance (EBS) is an essential component of Early Warning Alert and Response (EWAR) as per the International Health Regulations (IHR), 2005. EBS was established in Sudan in 2016 as a complementary system for Indicator-based surveillance (IBS). This review will provide an overview of the current EBS structure, functions and performance in Sudan and identify the gaps and ways forward.  METHODS: The review followed the WHO/EMRO guidelines and tools. Structured discussions, observation and review of records and guidelines were done at national and state levels. Community volunteers were interviewed through phone calls. Directors of Health Emergency and Epidemic Control, surveillance officers and focal persons for EBS at the state level were also interviewed. SPSS software was used to perform descriptive statistical analysis for quantitative data, while qualitative data was analysed manually using thematic analysis, paying particular attention to the health system level allowing for an exploration of how and why experiences differ across levels. Written and verbal consents were obtained from all participants as appropriate. RESULTS: Sudan has a functioning EBS; however, there is an underestimation of its contribution and importance at the national and states levels. The link between the national level and states is ad hoc or is driven by the need for reports. While community event-based surveillance (CEBS) is functioning, EBS from health facilities and from non-health sectors is not currently active. The integration of EBS into overall surveillance was not addressed, and the pathway from detection to action is not clear. The use of electronic databases and platforms is generally limited. Factors that would improve performance include training, presence of a trained focal person at state level, and regular follow-up from the national level. Factors such as staff turnover, income in relation to expenses and not having a high academic qualification (Diploma or MSc) were noticed as inhibiting factors. CONCLUSION: The review recommended revisiting the surveillance structure at national and state levels to put EBS as an essential component and to update guidelines and standard operation procedures SOPs to foster the integration between EBS components and the overall surveillance system. The need for strengthening the link with states, capacity building and re-addressing the training modalities was highlighted.

Incentives, lockdown, and testing: from Thucydides' analysis to the COVID-19 pandemic.

In this work, we provide a general mathematical formalism to study the optimal control of an epidemic, such as the COVID-19 pandemic, via incentives to lockdown and testing. In particular, we model the interplay between the government and the population as a principal-agent problem with moral hazard, à la Cvitanic et al. (Finance Stoch 22(1):1-37, 2018), while an epidemic is spreading according to dynamics given by compartmental stochastic SIS or SIR models, as proposed respectively by Gray et al. (SIAM J Appl Math 71(3):876-902, 2011) and Tornatore et al. (Phys A Stat Mech Appl 354(15):111-126, 2005). More precisely, to limit the spread of a virus, the population can decrease the transmission rate of the disease by reducing interactions between individuals. However, this effort-which cannot be perfectly monitored by the government-comes at social and monetary cost for the population. To mitigate this cost, and thus encourage the lockdown of the population, the government can put in place an incentive policy, in the form of a tax or subsidy. In addition, the government may also implement a testing policy in order to know more precisely the spread of the epidemic within the country, and to isolate infected individuals. In terms of technical results, we demonstrate the optimal form of the tax, indexed on the proportion of infected individuals, as well as the optimal effort of the population, namely the transmission rate chosen in response to this tax. The government's optimisation problems then boils down to solving an Hamilton-Jacobi-Bellman equation. Numerical results confirm that if a tax policy is implemented, the population is encouraged to significantly reduce its interactions. If the government also adjusts its testing policy, less effort is required on the population side, individuals can interact almost as usual, and the epidemic is largely contained by the targeted isolation of positively-tested individuals.

Illuminated border: Spatiotemporal analysis of COVID-19 pressure in the Sino-Burma border from the perspective of nighttime light.

The emergence of mutant strains such as Omicron has increased the uncertainty of COVID-19, and all countries have taken strict measures to prevent the spread of the disease. The spread of the disease between countries is of particular concern. However, most COVID-19 research focuses mainly on the country or community, and there is less research on the border areas between two countries. In this study, we analyzed changes in the total nighttime light intensity (TNLI) and total nighttime lit area (TNLA) along the Sino-Burma border and used the data to construct an epidemic pressure input index (PII) model in reference to the Shen potential model. The results show that, as the epidemic became more severe, TNLI on both sides of the border at the Ruili border port increased, while that in areas far from the port decreased. At the same time, increases and decreases in TNLA occurred in areas far from the port, and PII can indicate the areas where imported cases are likely to occur. Along the Sino-Burma border, the PII model showed low PII in the north and south and high PII in the central region. The areas between Dehong and Lincang, especially the Ruili, Wanding, Nansan, and Qingshuihe border ports, had high PII. The results of this study offer a reference for public health officials and decision makers when determining resource allocation and the implementation of stricter quarantine rules. With updated epidemic statistics, PII can be recalculated to support timely monitoring of COVID-19 in border areas.

COVID-19 in the Czech Republic 2020 and 2021: comparative analysis of probable work-related transmission of the coronavirus SARS-CoV-2.

OBJECTIVES: The aim of the analysis was to determine the probable places of coronavirus transmission in association with the work and compare the situation between 2020 and 2021. METHODS: The work analysed data from the Information System of Infectious Diseases managed by the Institute of Health Information and Statistics of the Czech Republic in the period from March 2020 - December 2021. RESULTS: 2,483,219 COVID-19 cases were officially confirmed (732,202 during 2020 and 1,338,790 in 2021), from them 140,368 (6%) represented work-related disease, 520,830 cases (21%) work-related contact, and 1,822,021 (73%) out-of-work contact. There were identified 13 occupations with the highest incidence of COVID-19 in the observed period (458,341 cases), in descending order - clerk, machinist, teacher, craftsman, worker/agency worker, driver, sales worker/cashier, warehouse worker/expediter, nurse, manager, food worker, paramedic, and social worker. Comparing 2020 and 2021, there was a difference in the ranking of occupations by incidence of disease. In 2021, the risk of infection acquiring increased for the occupations clerk, machinist, craftsman, worker/agency worker, manager, and food worker, while it decreased for the health professions (nurse, other paramedic, physician) and for social worker; 5,514 cases of COVID-19 were recognized as an occupational disease in 2020 and 2021, from them 5,483 cases (99.4%) in the health and social care economic activity sector. CONCLUSION: The available data show probable exposures to an infectious agent (without proof of specific contact with the source of the infection), of which 27% cases of COVID-19 are related to work (cases of work-related disease and work-related contact represented together the closest relationship to work). Different relevant anti-epidemic measures in the workplace have considerable practical importance for epidemic control. The use of personal protection of the mouth and nose with respirators/muffs is essential to reduce the risk of airborne transmission.

A knowledge graph-based method for epidemic contact tracing in public transportation.

Contact tracing is an effective measure by which to prevent further infections in public transportation systems. Considering the large number of people infected during the COVID-19 pandemic, digital contact tracing is expected to be quicker and more effective than traditional manual contact tracing, which is slow and labor-intensive. In this study, we introduce a knowledge graph-based framework for fusing multi-source data from public transportation systems to construct contact networks, design algorithms to model epidemic spread, and verify the validity of an effective digital contact tracing method. In particular, we take advantage of the trip chaining model to integrate multi-source public transportation data to construct a knowledge graph. A contact network is then extracted from the constructed knowledge graph, and a breadth-first search algorithm is developed to efficiently trace infected passengers in the contact network. The proposed framework and algorithms are validated by a case study using smart card transaction data from transit systems in Xiamen, China. We show that the knowledge graph provides an efficient framework for contact tracing with the reconstructed contact network, and the average positive tracing rate is over 96%.

The economics of stop-and-go epidemic control.

We analyse 'stop-and-go' containment policies that produce infection cycles as periods of tight lockdowns are followed by periods of falling infection rates. The subsequent relaxation of containment measures allows cases to increase again until another lockdown is imposed and the cycle repeats. The policies followed by several European countries during the Covid-19 pandemic seem to fit this pattern. We show that 'stop-and-go' should lead to lower medical costs than keeping infections at the midpoint between the highs and lows produced by 'stop-and-go'. Increasing the upper and reducing the lower limits of a stop-and-go policy by the same amount would lower the average medical load. But increasing the upper and lowering the lower limit while keeping the geometric average constant would have the opposite effect. We also show that with economic costs proportional to containment, any path that brings infections back to the original level (technically a closed cycle) has the same overall economic cost.

The effects of Wuhan highway lockdown measures on the spread of COVID-19 in China.

To verify the effects of Wuhan highway lockdown measures on the spread of COVID-19 across China cities, we extracted the vehicle outflow from Wuhan to 245 cities from the Chinese highway toll system. A dynamic exponential risk model that considered the vehicle outflow, city gross domestic product, city population, and distance between two cities was established to characterize the spread of pandemics and quantify the blocking effects. Results showed that an early highway lockdown measure could indeed reduce the confirmed cases and vehicles with 1-9 seats played a leading role. The confirmed cases in Guangxi, Henan, and Shanxi could be reduced by more than 50%, as well as Hubei by 20% if the highway was closed 3 days in advance. The blocking effects on Fujian, Jiangxi, Guangdong, Hunan, and Shandong were not obvious, where the number of confirmed cases only decreased by a small proportion (below 10%). The findings could be used to help each provincial government to adjust policies properly and improve the effectiveness of epidemic control and prevention. Moreover, the proposed method could also be applied to various countries or regions affected by COVID-19, as well as other similar pandemics.

Analysis of optimal lockdown in integral economic-epidemic model.

We analyze the optimal lockdown in an economic-epidemic model with realistic infectiveness distribution. The model is described by Volterra integral equations and accurately depicts the COVID-19 infectivity pattern from clinical data. A maximum principle is derived, and a qualitative dynamic analysis of the optimal lockdown problem is provided over finite and infinite horizons. We analytically prove and economically justify the possibility of an endemic scenario when the infection rate begins to climb after the lockdown ends.

Invasive Bacterial Vaccine-Preventable Disease Surveillance: Successes and Lessons Learned in Bangladesh for a Sustainable Path Forward.

We have made considerable progress in setting and scaling up surveillance systems to drive evidence-based policy decisions, but the recent epidemics highlight that current systems are not optimally designed. Good surveillance systems should be coordinated, comprehensive, and adaptive. They should generate data in real time for immediate analysis and intervention, whether for endemic diseases or potential epidemics. Such systems are especially needed in low-resource settings where disease burden is the highest, but tracking systems are the weakest here due to competing priorities and constraints on available resources. In this article, using the examples of 3 large, and mostly successful, infectious disease surveillance studies in Bangladesh, we identify 2 core limitations-the pathogen bias and the vaccine bias-in the way current surveillance programs are designed for low-resource settings. We highlight the strengths of the current Global Invasive Bacterial Vaccine Preventable Disease Surveillance Network of the World Health Organization and present case studies from Bangladesh to illustrate how this surveillance platform can be leveraged to overcome its limitations. Finally, we propose a set of criteria for building a comprehensive infectious disease surveillance system with the hope of encouraging current systems to use the limited resources as optimally as possible to generate the maximum amount of knowledge.

[Improvement of legal and regulatory framework of epidemic control measures in bureau of forensic medicine]. / Sovershenstvovanie normativno-pravovogo regulirovaniya protivoepidemicheskikh meropriyatii v byuro sudebno-meditsinskoi ekspertizy.

The study objective is to improve epidemic control measures in the bureau of forensic medicine by reviewing current local regulatory documents, formulating proposals for their modification, and updating issues related to infectious safety and diagnostic accuracy of infectious diseases. Specific proposals have been provided for modifications of statistical forms for infectious disease recording, regulatory documents, and internal instructions.

The First 100 Days of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Control in Vietnam.

BACKGROUND: One hundred days after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in Vietnam on 23 January, 270 cases were confirmed, with no deaths. We describe the control measures used by the government and their relationship with imported and domestically acquired case numbers, with the aim of identifying the measures associated with successful SARS-CoV-2 control. METHODS: Clinical and demographic data on the first 270 SARS-CoV-2 infected cases and the timing and nature of government control measures, including numbers of tests and quarantined individuals, were analyzed. Apple and Google mobility data provided proxies for population movement. Serial intervals were calculated from 33 infector-infectee pairs and used to estimate the proportion of presymptomatic transmission events and time-varying reproduction numbers. RESULTS: A national lockdown was implemented between 1 and 22 April. Around 200 000 people were quarantined and 266 122 reverse transcription polymerase chain reaction (RT-PCR) tests conducted. Population mobility decreased progressively before lockdown. In total, 60% (163/270) of cases were imported; 43% (89/208) of resolved infections remained asymptomatic for the duration of infection. The serial interval was 3.24 days, and 27.5% (95% confidence interval [CI], 15.7%-40.0%) of transmissions occurred presymptomatically. Limited transmission amounted to a maximum reproduction number of 1.15 (95% CI, .·37-2.·36). No community transmission has been detected since 15 April. CONCLUSIONS: Vietnam has controlled SARS-CoV-2 spread through the early introduction of mass communication, meticulous contact tracing with strict quarantine, and international travel restrictions. The value of these interventions is supported by the high proportion of asymptomatic and imported cases, and evidence for substantial presymptomatic transmission.

Differential impact of non-pharmaceutical public health interventions on COVID-19 epidemics in the United States.

BACKGROUND: The widespread pandemic of novel coronavirus disease 2019 (COVID-19) poses an unprecedented global health crisis. In the United States (US), different state governments have adopted various combinations of non-pharmaceutical public health interventions (NPIs), such as non-essential business closures and gathering bans, to mitigate the epidemic from February to April, 2020. Quantitative assessment on the effectiveness of NPIs is greatly needed to assist in guiding individualized decision making for adjustment of interventions in the US and around the world. However, the impacts of these approaches remain uncertain. METHODS: Based on the reported cases, the effective reproduction number (Rt) of COVID-19 epidemic for 50 states in the US was estimated. Measurements on the effectiveness of nine different NPIs were conducted by assessing risk ratios (RRs) between Rt and NPIs through a generalized linear model (GLM). RESULTS: Different NPIs were found to have led to different levels of reduction in Rt. Stay-at-home contributed approximately 51% (95% CI 46-57%), wearing (face) masks 29% (15-42%), gathering ban (more than 10 people) 19% (14-24%), non-essential business closure 16% (10-21%), declaration of emergency 13% (8-17%), interstate travel restriction 11% (5-16%), school closure 10% (7-14%), initial business closure 10% (6-14%), and gathering ban (more than 50 people) 7% (2-11%). CONCLUSIONS: This retrospective assessment of NPIs on Rt has shown that NPIs played critical roles on epidemic control in the US in the past several months. The quantitative results could guide individualized decision making for future adjustment of NPIs in the US and other countries for COVID-19 and other similar infectious diseases.