A local COVID-19 outbreak with two community clusters occurred in a large industrial city, Shaoxing, China, in December 2021 after serial interventions were imposed. We aimed to understand the reason by analysing the characteristics of the outbreak and evaluating the effects of phase-adjusted interventions. Publicly available data from 7 December 2021 to 25 January 2022 were collected to analyse the epidemiological characteristics of this outbreak. The incubation period was estimated using Hamiltonian Monte Carlo method. A well-fitted extended susceptible-exposed-infectious-recovered model was used to simulate the impact of different interventions under various combination of scenarios. There were 387 SARS-CoV-2-infected cases identified, and 8.3% of them were initially diagnosed as asymptomatic cases. The estimated incubation period was 5.4 (95% CI 5.2–5.7) days for all patients. Strengthened measures of comprehensive quarantine based on tracing led to less infections and a shorter duration of epidemic. With a same period of incubation, comprehensive quarantine was more effective in containing the transmission than other interventions. Our findings reveal an important role of tracing and comprehensive quarantine in blocking community spread when a cluster occurred. Regions with tense resources can adopt home quarantine as a relatively affordable and low-impact intervention measure compared with centralized quarantine.

The evidence for the incubation period of Legionnaires’ disease is based on data from a small number of outbreaks. An incubation period of 2–10 days is commonly used for the definition and investigation of cases. In the German LeTriWa study, we collaborated with public health departments to identify evidence-based sources of exposure among cases of Legionnaires’ disease within 1–14 days before symptom onset. For each individual, we assigned weights to the numbered days of exposure before symptom onset, giving the highest weight to exposure days of cases with only one possible day of exposure. We then calculated an incubation period distribution where the median was 5 days and the mode was 6 days. The cumulative distribution reached 89% by the 10th day before symptom onset. One case-patient with immunosuppression had a single day of exposure to the likely infection source only 1 day before symptom onset. Overall, our results support the 2- to 10-day incubation period used in case definition, investigation, and surveillance of cases with Legionnaires’ disease.

Quantitative information on epidemiological quantities such as the incubation period and generation time of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants is scarce. We analysed a dataset collected during contact tracing activities in the province of Reggio Emilia, Italy, throughout 2021. We determined the distributions of the incubation period for the Alpha and Delta variants using information on negative polymerase chain reaction tests and the date of last exposure from 282 symptomatic cases. We estimated the distributions of the intrinsic generation time using a Bayesian inference approach applied to 9724 SARS-CoV-2 cases clustered in 3545 households where at least one secondary case was recorded. We estimated a mean incubation period of 4.9 days (95% credible intervals, CrI, 4.4–5.4) for Alpha and 4.5 days (95% CrI 4.0–5.0) for Delta. The intrinsic generation time was estimated to have a mean of 7.12 days (95% CrI 6.27–8.44) for Alpha and of 6.52 days (95% CrI 5.54–8.43) for Delta. The household serial interval was 2.43 days (95% CrI 2.29–2.58) for Alpha and 2.74 days (95% CrI 2.62–2.88) for Delta, and the estimated proportion of pre-symptomatic transmission was 48–51% for both variants. These results indicate limited differences in the incubation period and intrinsic generation time of SARS-CoV-2 variants Alpha and Delta compared to ancestral lineages.

Vector-borne parasites are important ecological drivers influencing life-history evolution in birds by increasing host mortality or susceptibility to new diseases. Therefore, understanding why vulnerability to infection varies within a host clade is a crucial task for conservation biology and for understanding macroecological life-history patterns. Here, we studied the relationship of avian life-history traits and climate on the prevalence of Plasmodium and Parahaemoproteus parasites. We sampled 3569 individual birds belonging to 53 species of the family Thraupidae. Individuals were captured from 2007 to 2018 at 92 locations. We created 2 phylogenetic generalized least-squares models with Plasmodium and Parahaemoproteus prevalence as our response variables, and with the following predictor variables: climate PC1, climate PC2, body size, mixed-species flock participation, incubation period, migration, nest height, foraging height, forest cover, and diet. We found that Parahaemoproteus and Plasmodium prevalence was higher in species inhabiting open habitats. Tanager species with longer incubation periods had higher Parahaemoproteus prevalence as well, and we hypothesize that these longer incubation periods overlap with maximum vector abundances, resulting in a higher probability of infection among adult hosts during their incubation period and among chicks. Lastly, we found that Plasmodium prevalence was higher in species without migratory behaviour, with mixed-species flock participation, and with an omnivorous or animal-derived diet. We discuss the consequences of higher infection prevalence in relation to life-history traits in tanagers.

In this paper, we propose and study an almost periodic reaction–diffusion epidemic model in which disease latency, spatial heterogeneity and general seasonal fluctuations are incorporated. The model is given by a spatially nonlocal reaction–diffusion system with a fixed time delay. We first characterise the upper Lyapunov exponent λ* for a class of almost periodic reaction–diffusion equations with a fixed time delay and provide a numerical method to compute it. On this basis, the global threshold dynamics of this model is established in terms of λ* It is shown that the disease-free almost periodic solution is globally attractive if λ* < 0, while the disease is persistent if λ* > 0. By virtue of numerical simulations, we investigate the effects of diffusion rate, incubation period and spatial heterogeneity on disease transmission.

This study estimates the incubation period of COVID-19 among locally transmitted cases, and its association with age to better inform public health measures in containing COVID-19. Epidemiological data of all PCR-confirmed COVID-19 cases from all restructured hospitals in Singapore were collected between 23 January 2020 and 2 April 2020. Activity mapping and detailed epidemiological investigation were conducted by trained personnel. Positive cases without clear exposure to another positive case were excluded from the analysis. One hundred and sixty-four cases (15.6% of patients) met the inclusion criteria during the defined period. The crude median incubation period was 5 days (range 1–12 days) and median age was 42 years (range 5–79 years). The median incubation period among those 70 years and older was significantly longer than those younger than 70 years (8 vis-à-vis 5 days, P = 0.040). Incubation period was negatively correlated with day of illness in both groups. These findings support current policies of 14-day quarantine periods for close contacts of confirmed cases and 28 days for monitoring infections in known clusters. An elderly person who may have a longer incubation period than a younger counterpart may benefit from earlier and proactive testing, especially after exposure to a positive case.

A novel coronavirus disease, designated as COVID-19, has become a pandemic worldwide. This study aims to estimate the incubation period and serial interval of COVID-19. We collected contact tracing data in a municipality in Hubei province during a full outbreak period. The date of infection and infector–infectee pairs were inferred from the history of travel in Wuhan or exposed to confirmed cases. The incubation periods and serial intervals were estimated using parametric accelerated failure time models, accounting for interval censoring of the exposures. Our estimated median incubation period of COVID-19 is 5.4 days (bootstrapped 95% confidence interval (CI) 4.8–6.0), and the 2.5th and 97.5th percentiles are 1 and 15 days, respectively; while the estimated serial interval of COVID-19 falls within the range of −4 to 13 days with 95% confidence and has a median of 4.6 days (95% CI 3.7–5.5). Ninety-five per cent of symptomatic cases showed symptoms by 13.7 days (95% CI 12.5–14.9). The incubation periods and serial intervals were not significantly different between male and female, and among age groups. Our results suggest a considerable proportion of secondary transmission occurred prior to symptom onset. And the current practice of 14-day quarantine period in many regions is reasonable.

Shiga-toxin producing Escherichia coli (STEC) is a pathogen that can cause bloody diarrhoea and severe complications. Cases occur sporadically but outbreaks are also common. Understanding the incubation period distribution and factors influencing it will help in the investigation of exposures and consequent disease control. We extracted individual patient data for STEC cases associated with outbreaks with a known source of exposure in England and Wales. The incubation period was derived and cases were described according to patient and outbreak characteristics. We tested for heterogeneity in reported incubation period between outbreaks and described the pattern of heterogeneity. We employed a multi-level regression model to examine the relationship between patient characteristics such as age, gender and reported symptoms; and outbreak characteristics such as mode of transmission with the incubation period. A total of 205 cases from 41 outbreaks were included in the study, of which 64 cases (31%) were from a single outbreak. The median incubation period was 4 days. Cases reporting bloody diarrhoea reported shorter incubation periods compared with cases without bloody diarrhoea, and likewise, cases aged between 40 and 59 years reported shorter incubation period compared with other age groups. It is recommended that public health officials consider the characteristics of cases involved in an outbreak in order to inform the outbreak investigation and the period of exposure to be investigated.

Hepatitis E virus genotype 1 (HEV G1) is an important cause of morbidity and mortality in Africa and Asia. HEV G1's natural history, including the incubation period, remains poorly understood, hindering surveillance efforts and effective control. Using individual-level data from 85 travel-related HEV G1 cases in England and Wales, we estimate the incubation period distribution using survival analysis methods, which allow for appropriate inference when only time ranges, rather than exact times are known for the exposure to HEV and symptom onset. We estimated a 29.8-day (95% confidence interval (CI) 24.1–36.0) median incubation period with 5% of people expected to develop symptoms within 14.3 days (95% CI 10.1–21.7) and 95% within 61.9 days (95% CI 47.4–74.4) of exposure. These estimates can help refine clinical case definitions and inform the design of disease burden and intervention studies.

Estimates of the incubation period for Q fever vary substantially between different reviews and expert advice documents. We systematically reviewed and quality appraised the literature to provide an evidence-based estimate of the incubation period of the Q fever by the aerosolised infection route. Medline (OVIDSP) and EMBASE were searched with the search limited to human studies and English language. Eligible studies included persons with symptomatic, acute Q fever, and defined exposure to Coxiella burnetti. After review of 7115 titles and abstracts, 320 records were screened at full-text level. Of these, 23 studies contained potentially useful data and were quality assessed, with eight studies (with 403 individual cases where the derivation of incubation period was possible) being of sufficient quality and providing individual-level data to produce a pooled summary. We found a median incubation period of 18 days, with 95% of cases expected to occur between 7 and 32 days after exposure.

Incubation period for non-typhoidal Salmonella (NTS) infections is generally reported as 6–72 h despite numerous reports of foodborne NTS outbreaks with median incubation periods >3 days. We summarised 16 years of Minnesota foodborne NTS outbreaks to better estimate the expected range of incubation periods for NTS infections. Of the 1517 NTS outbreak cases, 725 had enough data to calculate a precise incubation period. The median incubation period was 45 h; 77 (11%) cases had incubations ⩽12 h and 211 (29%) cases had incubations >72 h. Incubation period length varied by outbreak vehicle type, Salmonella serotype and outbreak setting. Based on our data, a more accurate description would be that the incubation of NTS infection is usually from 12 to 96 h, that incubations in >96 to 144 h (>4 to 6-day) range are not unusual and that incubations from 7 to 9 days and occasionally longer also occur.

Infections with the malaria parasite Plasmodium vivax are noteworthy for potentially very long incubation periods (6–9 months), which present a major barrier to disease elimination. Increased sporozoite challenge has been reported to be associated with both shorter incubation and pre-patent periods in a range of human challenge studies. However, this evidence base has scant empirical foundation, as these historical analyses were limited by available analytic methods, and provides no quantitative estimates of effect size. Following a comprehensive literature search, we re-analysed all identified studies using survival and/or logistic models plus contingency tables. We have found very weak evidence for dose-dependence at entomologically plausible inocula levels. These results strongly suggest that sporozoite dosage is not an important driver of long-latency. Evidence presented suggests that parasite strain and vector species have quantitatively greater impacts, and the potential existence of a dose threshold for human dose-response to sporozoites. Greater consideration of the complex interplay between these aspects of vectors and parasites are important for human challenge experiments, vaccine trials, and epidemiology towards global malaria elimination.

Optimization of the cultivation conditions and medium components for folate production by the highest folate producing Streptococcus thermophilus strain RD102 was carried out using a 23 central composite design and surface modelling method of response surface methodology. As folate production was observed to be growth-associated, hence the desired responses selected for the optimization were both folate production and growth. The selected factors based on preliminary investigations were incubation period, concentrations of p-amino benzoic acid (PABA) the growth precursor, and lactose as carbon source. The optimum concentrations of PABA and lactose were found to be 300 μm and 3% respectively at 72 h incubation. The optimized conditions resulted in an increase in folate production by 26% compared with control conditions (0% PABA and lactose at 37°C). Using the method of experimental factorial design and response surface analysis, it was possible to determine optimal operating conditions to obtain a higher folate production by Strep. thermophilus. Therefore, this study constitutes a step in developing strategies to modulate the folate level to a higher level.

The size of the infecting inoculum may influence the severity of malaria, but evidence is scarce. Malaria therapy records provide a unique source of information on induced malaria in people. The therapy was given to large numbers of neurosyphilis patients and the malaria was left untreated as long as possible. Data from patients treated at the Horton Hospital, Epsom 1923–60 with a single strain of vivax malaria were analysed to assess the influence of inoculum size on severity of disease. Malaria was induced by mosquito bite, blood inoculation or direct sporozoite inoculation. The different measures of inoculum size were inversely correlated with pre-patent period, as expected. Overall, information was available on a total of 563 non-immune patients who were not treated during the first 5 days of patent parasitaemia. No strong or consistent relationships were found between measures of inoculum size and any of the measures of severity used: neither parasitaemia levels, nor peak fevers, nor number of paroxysms of fever. In the largest data set, longer pre-patent periods were associated with tertian fever, spontaneous recovery and less use of modifying treatment. Difficulties in interpreting the results are discussed, particularly with respect to misclassification of both exposure and outcome variables. While an inoculum size-severity relationship cannot be ruled out, a strong relationship is very unlikely.

The cotton boll worm, Heliothis armigera Hbn, was grown under standard conditions. Parameters are given for identification of larval instars on the basis of head capsule width. Intrastadial weight of larvae in terms of fresh weight is presented for a regular time interval of 12 hr. The maximum larval growth in all the stadia was observed in the first 24 hr after the preceding moult. Weight of larvae was almost constant in the last quarter of the stadium, till they moulted into the next. Although the last instar larvae gained maximum weight amongst all the stadia, their actual growth was minimum. First instar larvae recorded a 13 times increase in body weight in comparison to the last instar larvae with 3.9 times. Fifth instar larvae entered into the prepupal stage after they had completed an age of 60 hr. Temperature had a profound influence on the incubation period of eggs; the higher the temperature, the shorter it was. Between 28 and 36°C the incubation period was almost the same. The maximum tolerance level of constant temperature for the eggs of Heliothis armigera was 37°C.