A data-driven approach to measuring epidemiological susceptibility risk around the world

Epidemic outbreaks are extreme events that become more frequent and severe, associated with large social and real costs. It is therefore important to assess whether countries are prepared to manage epidemiological risks. We use a fully data-driven approach to measure epidemiological susceptibility risk at the country level using time-varying information. We apply both principal component analysis (PCA) and dynamic factor model (DFM) to deal with the presence of strong cross-section dependence in the data. We conduct extensive in-sample model evaluations of 168 countries covering 17 indicators for the 2010–2019 period. The results show that the robust PCA method accounts for about 90% of total variability, whilst the DFM accounts for about 76% of the total variability. Our index could therefore provide the basis for developing risk assessments of epidemiological risk contagion. It could be also used by organizations to assess likely real consequences of epidemics with useful managerial implications.

Epidemiology, Haemato-biochemical and Pathological Changes Related to Field Outbreaks of PPR in Small Ruminants in Odisha

Background: Odisha experiencing sporadic outbreaks of Peste des petits ruminants (PPR) throughout the year. There is a scarcity of available literature on PPR in Odisha till today. This is the first ever detail investigative approach in the state undertaken with an objective to corelate the epidemiological risk factors, haemato-biochemical and pathological changes in natural field outbreaks occurring in eight different districts. Methods: Fourteen field outbreaks of PPR were evaluated clinically as well as epidemiologically and confirmed through polymerase chain reaction (PCR). Blood, serum, faecal and tissue samples were collected to observe haemato-biochemical and pathomorphological changes to asses disease severity. Result: Present study concluded an overall mortality rate of 46.81%. Chi-square analysis revealed significant highest prevalence among 7-12 months (46.13%) age, Ganjam breed (45.51%) and females (80.49%). Frequent migration among the border areas along with poor management and helminthic infection was major precipitating factor. There was polycythemia along with neutrophilia and lymphopenia. Significant increase in alanine transaminase (ALT), aspartate aminotransferase (AST), K+ and Ca+2 along with creatinine, urea and blood urea nitrogen (BUN) BUN was observed in affected flocks. Antero-ventral consolidation of lungs, syncytia and presence of both eosinophilic intranuclear and intracytoplasmic inclusion bodies were major pathological changes.

Comprehensive epithelial tubo-ovarian cancer risk prediction model incorporating genetic and epidemiological risk factors

BackgroundEpithelial tubo-ovarian cancer (EOC) has high mortality partly due to late diagnosis. Prevention is available but may be associated with adverse effects. A multifactorial risk model based on known genetic and epidemiological risk factors (RFs) for EOC can help identify women at higher risk who could benefit from targeted screening and prevention.MethodsWe developed a multifactorial EOC risk model for women of European ancestry incorporating the effects of pathogenic variants (PVs) in BRCA1, BRCA2, RAD51C, RAD51D and BRIP1, a Polygenic Risk Score (PRS) of arbitrary size, the effects of RFs and explicit family history (FH) using a synthetic model approach. The PRS, PV and RFs were assumed to act multiplicatively.ResultsBased on a currently available PRS for EOC that explains 5% of the EOC polygenic variance, the estimated lifetime risks under the multifactorial model in the general population vary from 0.5% to 4.6% for the first to 99th percentiles of the EOC risk distribution. The corresponding range for women with an affected first-degree relative is 1.9%–10.3%. Based on the combined risk distribution, 33% of RAD51D PV carriers are expected to have a lifetime EOC risk of less than 10%. RFs provided the widest distribution, followed by the PRS. In an independent partial model validation, absolute and relative 5-year risks were well calibrated in quintiles of predicted risk.ConclusionThis multifactorial risk model can facilitate stratification, in particular among women with FH of cancer and/or moderate-risk and high-risk PVs. The model is available via the CanRisk Tool (www.canrisk.org).

Investigating Critical Risk Factors of Liver Cancer with Deep Neural Networks

The crude incidence of liver cancer ranks top five among all cancers in China, and the death rate ranks the top two. Identifying critical risk factors of liver cancer helps people adjust their lifestyles to reduce cancer risk. Launched in 2012, Early Diagnosis and Treatment of Urban Cancer project has been carried out in major cities of China, which collected a broad range of epidemiological risk factors including definite, probable and possible causes of cancer. We retrieved data from 2014 to the present and obtained 184 liver cancer cases among 55 thousand people. We explored 84 risk factors and implemented liver cancer prediction model with machine learning algorithms, where deep neural network achieved the best performance using non-clinical information (mean AUC=0.73). We analyzed model parameters to investigate critical risk factors that contribute the most to prediction. Using 50% top-ranking risk factors to train a model, the performance showed no significant difference from that using all risk factors. Using top 10% risk factors induced a sensitivity drop and a lower false positive rate. These phenomena prove that the identified risk factors are critical in liver cancer prediction. This work is a reference in public health research, and provides a scientific lifestyle guideline for individuals to prevent liver cancer based on machine learning technology.

Impact of a public health intervention for active surveillance and mitigation of SARS-CoV-2 in a district from Buenos Aires province, Argentina: a descriptive epidemiological study

BackgroundThe first case of SARS-CoV-2 was reported in Argentina on 3 March 2020. Measures to limit the spread of the virus were implemented, including complete lockdown (26 March). Nonetheless, the virus spread throughout the country, with a first peak of almost a million cases in October. On 30 November, the government’s recommendation switched from social, preventive and compulsory isolation, to social, preventive and compulsory distancing.ObjectivesTo describe a tailored public health strategy to mitigate the spread of SARS-CoV-2 and determine its behaviour in San Antonio de Areco district from Buenos Aires province (Argentina) through a private–public association.Design, setting and participantsSurveillance of the virus was performed with the local healthcare system, through early identification of cases and the systematic study of each infected individual and contact, regardless of symptomatology, using telemedicine and a COVID-19-specific outpatient clinic. Real-time PCR was used for detection using both individual and pooled samples, with a 12-hour turnaround time.ResultsUp to 30 November, a total of 2426 suspected cases were analysed and 578 were confirmed. Surveillance of health personnel and at-risk populations proved effective, mitigating viral spread. Pooling samples allowed reduction of operator time, helped reduce costs, and allowed detection of both symptomatic and asymptomatic cases.ConclusionAfter 8 months of protocol implementation, the strategy to intensively survey groups at higher epidemiological risk and the systematic search for asymptomatic cases with the incorporation of pooled PCR for diagnosis, in combination with individual testing, is an efficient and viable option in populations with similar characteristics, in the frame of social isolation.

Methodological development for teaching environmental management within the framework of the university research and teaching project in the Central of Heredia (March to September 2020), Costa Rica. A preliminary study

In January 2020, the “Urban and social reconfiguration project of Heredia, and youth population, 1985-2020” starts its operations. Its objective is to analyze the urban capacities for social life, and the imprint of young people has developed between 1985 to 2020 in Heredia. Thus, to achieve the proposed goals, a methodology is established that joins research and teaching areas. Consequences of institutional policies impact this methodology to come from a national emergency of Covid-19, which causes the substantial methodological changes of the project. This preliminary study aims to communicate the methodological challenges in teaching environmental management in the current context. The method is interviews applied to students and academics participating in the project, consultation of secondary sources, records, databases, institutional reports, and observation of areas representing a less epidemiological risk to researchers. The conclusions of this experience are the following: (i) changes in the techniques of the methods and tools used in science; (ii) resilience of academic staff to take on extra hours; development of new learning technologies; and (iii) approach to the pedagogical discussion related to the project in the current conditions, which it makes a solid and enriching panorama for academic issues.

Empiric Antimicrobial Therapy in Critically Ill Septic Patients

Sepsis is a medical emergency and life-threatening condition due to a dysregulated host response to infection, which is time-dependent and associated with unacceptably high mortality. At the bedside of a patient with sepsis or septic shock, clinician must make immediate life-saving decisions including empirical initiation of broad-spectrum antimicrobials; the most likely to be appropriate. The empiric regimen should be initiated within the first hour of diagnosis and determined by assessing patient and epidemiological risk factors, likely source of infection based on presenting signs and symptoms, and severity of illness. Optimizing antibiotic use is crucial to ensure successful outcomes and to reduce adverse antibiotic effects, as well as preventing drug resistance. All likely pathogens involved should be considered to provide an appropriate antibiotic coverage. Herein, we tried to make suggestions of empirical therapeutic regimens in sepsis/septic shock according to most likely pathogens in cause and sepsis source based on the recent recommendations of learned societies. Some suggestions were adapted to an environment of low-resource regions where the ecology of multi drug resistant organisms is of concern.

Pathogens co-transported with invasive non-native aquatic species: implications for risk analysis and legislation

Invasive Non-Native Species (INNS) can co-transport externally and internally other organisms including viruses, bacteria and other eukaryotes (including metazoan parasites), collectively referred to as the symbiome. These symbiotic organisms include pathogens, a small minority of which are subject to surveillance and regulatory control, but most of which are currently unscrutinized and/or unknown. These putatively pathogenetic symbionts can potentially pose diverse risks to other species, with implications for increased epidemiological risk to agriculture and aquaculture, wildlife/ecosystems, and human health (zoonotic diseases). The risks and impacts arising from co-transported known pathogens and other symbionts of unknown pathogenic virulence, remain largely unexplored, unlegislated, and difficult to identify and quantify. Here, we propose a workflow using PubMed and Google Scholar to systematically search existing literature to determine any known and potential pathogens of aquatic INNS. This workflow acts as a prerequisite for assessing the nature and risk posed by co-transported pathogens of INNS; of which a better understanding is necessary to inform policy and INNS risk assessments. Addressing this evidence gap will be instrumental to devise an appropriate set of statutory responsibilities with respect to these symbionts, and to underpin new and more effective legislative processes relating to the disease screening and risk assessment of INNS.

The value of the analysis of the urinary stones for studying the features of urolithiasis pathogenesis

Introduction. Urolithiasis is a multifactorial recurrent disease, unevenly spread throughout the world and characterizedby the formation of urinary stones of various chemical compositions, depending on pathogenesis, etiological, and epidemiological risk factors. Understanding the composition of chemicals and their prevailing ratios can help make decisions about treatment tactics, preventive measures to reduce the risk of recurrence and the prevalence of urolithiasis.Purpose of the study. To assess the distribution of chemical components in urinary stones along with an analysis of their population significance.Materials and methods. The urinary stones were obtained from 2854 patients with urolithiasis. The composition of urinary stones was analyzed by using an infrared spectroscopy method.Results. The predominance of oxalate stones was determined in multicomponent kidney stones (83,7%) and the prevalence of urate stones (54,2%) was revealed in monocomponent kidney stones. Urinary stones with a predominance of oxalates contained significantly fewer impurities (12.4%) than urinary stones with a predominance of urates, phosphates and carbonates with an average amount of impurities >24.0%.Conclusion. The analysis of urinary stones distribution based on pathogenic factors showed that the calcium metabolism disturbances prevail in the population of the Russian Federation (88.0%).