The Prediction Value of the Infection Probability Score (IPS) Combined with Serum Cholinesterase and D-Dimer Detection for Infection and Survival in Critically Ill Patients

Objective: To evaluate early prediction value of IPS combined with SchE and D-dimer detection for infection and survival in critically ill patients. Methods: 199 critically ill patients admitted to the emergency intensive care unit (EICU) of our hospital from December 2018 to December 2019 were retrospectively analyzed, including 110 infection patients (infection group) and 89 non-infection patients (non-infection group). According to the survival, the infection group was divided into death group (68 cases) and survival group (42 cases). The IPS, APACHE II, SOFA and SchE, D-dimer expression levels were detected and compared;Univariate and logistic regression analysis were used to evaluate the independent prognostic factors. Results: The IPS and APACHE II of patients in the infected group were higher than those in the non-infected group, the level of SchE was lower than that in the non-infected group, and the level of D-dimer was higher than that in the non-infected group (P < 0.001). IPS, SOFA, APACHE II, SchE, D-dimer, invasive mechanical ventilation, septic shock, and ICU length of stay had significant influence on the prognosis of critically ill patients (P < 0.001). Logistic regression analysis showed that IPS (OR = 2.821, 95% CI 1.501 - 5.227), SOFA (OR = 5.078, 95% CI 3.327 - 7.690), APACHE II (OR = 14.308, 95% CI 8.901 - 21.893), SchE (OR = 0.223, 95% CI 0.165 - 0.291), D-dimer (OR = 2.10, 95% CI 1.55 - 2.85), septic shock (OR = 9.948, 95% CI 7.012 - 17.012) were independent factors affecting the prognosis of critically ill patients with infection (P < 0.001). Conclusion: IPS and D-dimer expression level in infected patients were increased and SchE decreased significantly compared with those in non-infected patients, and they significantly correlated with disease severity of infected patients and could be early prediction for prognosis.

Integrating host biological and ecological variables to predict probability of haemosporidian infection in raptors

Variations in host traits that influence their exposure and susceptibility may impact probability of vector-transmitted diseases.Therefore,identifying the predictors of infection probability is necessary to understand the risk of disease outbreaks during expanding environmental perturbation.Here,we conducted a large survey based on microscopic examination and molecular analysis of haemosporidian parasite infection in raptors rescued at the Beijing Raptor Rescue Centre.Combining these data with biological and ecological variables of the raptors,we determined predictors that affect the probability of haemosporidian infection using generalized linear mixed models and multimodel inference.Our results showed that infection probability exhibited considerable variation across host species in raptors,and body mass,sex,and evolutionary history played relatively weaker roles in driving infection probability.Instead,activity pattern,age,geographic range size,migration distance,and nest type were important predictors of the probability of haemosporidian infection,and the role of each predictor differed in the three main haemosporidian genera(Plasmodium,Haemoproteus,and Leucocytozoon).This macro-ecological analysis will add to our understanding of host traits that influence the probability of avian haemosporidian infection and will help inform risk of emerging diseases.

Probability Distribution of SARS-Cov-2 (COVID) Infectivity Following Onset of Symptoms: Analysis from First Principles

The phasing out of protective measures by governments and public health agencies, despite continued seriousness of the coronavirus pandemic, leaves individuals who are concerned for their health with two basic options over which they have control: 1) minimize risk of infection by being vaccinated and by wearing a face mask when appropriate, and 2) minimize risk of transmission upon infection by self-isolating. For the latter to be effective, it is essential to have an accurate sense of the probability of infectivity as a function of time following the onset of symptoms. Epidemiological considerations suggest that the period of infectivity follows a lognormal distribution. This proposition is tested empirically by construction of the lognormal probability density function and cumulative distribution function based on quantiles of infectivity reported by several independent investigations. A comprehensive examination of a prototypical ideal clinical study, based on general statistical principles (the Principle of Maximum Entropy and the Central Limit Theorem) reveals that the probability of infectivity is a lognormal random variable. Subsequent evolution of new variants may change the parameters of the distribution, which can be updated by the methods in this paper, but the form of the probability function is expected to remain lognormal as this is the most probable distribution consistent with mathematical requirements and available information.

Assessment of immunization procedures for foot-and-mouth disease in large-scale pig farms in China based on actual data and dynamics

Foot-and-mouth disease(FMD)is an acute,highly infectious and pathogenic animal disease.In recent years,with the rapid development of the swine breeding industry in China,pig farms have shown a trend of larger-scale development.Large-scale pig farms employ standardized management,a high level of automation,and a strict_system.However,these farms have a large trading volume,and increased transmission intensity of FMD is noted inside the farm.At present,the main control measure against FMD is pig vaccination.However,a standard for immunization procedures is not available,and currently adopted immunization procedures have not been effectively and systematically evaluated.Taking a typical large-scale pig farm in China as the research subject and considering the breeding pattern,piggery structure,age structure and immunization procedures,an individual-based state probability model is established to evaluate the effectiveness of the immune procedure.Based on numerical simulation,it is concluded that the optimal immunization program involves primary immunization at 40 days of age and secondary immunization at 80 days of age for commercial pigs.Breeding boars and breeding sows are immunized 4 times a year,and reserve pigs are immunized at 169 and 259 days of age.According to the theoretical analysis,the average control reproduction number of individuals under the optimal immunization procedure in the farm is 0.4927.In the absence of immunization,the average is 1.7498,indicating that the epidemic cannot be controlled without immunization procedures.