Project description:During sporogony, malaria-causing parasites infect a mosquito, reproduce and migrate to the mosquito salivary glands where they can be transmitted the next time blood feeding occurs. The time required for sporogony, known as the extrinsic incubation period (EIP), is an important determinant of malaria transmission intensity. The EIP is typically estimated as the time for a given percentile, x, of infected mosquitoes to develop salivary gland sporozoites (the infectious parasite life stage), which is denoted by EIPx. Many mechanisms, however, affect the observed sporozoite prevalence including the human-to-mosquito transmission probability and possibly differences in mosquito mortality according to infection status. To account for these various mechanisms, we present a mechanistic mathematical model, which explicitly models key processes at the parasite, mosquito and observational scales. Fitting this model to experimental data, we find greater variation in the EIP than previously thought: we estimated the range between EIP10 and EIP90 (at 27°C) as 4.5 days compared to 0.9 days using existing statistical methods. This pattern holds over the range of study temperatures included in the dataset. Increasing temperature from 21°C to 34°C decreased the EIP50 from 16.1 to 8.8 days. Our work highlights the importance of mechanistic modelling of sporogony to (1) improve estimates of malaria transmission under different environmental conditions or disease control programs and (2) evaluate novel interventions that target the mosquito life stages of the parasite.
Project description:The outbreak of a novel coronavirus (SARS-CoV-2) since December 2019 in Wuhan, the major transportation hub in central China, became an emergency of major international concern. While several etiological studies have begun to reveal the specific biological features of this virus, the epidemic characteristics need to be elucidated. Notably, a long incubation time was reported to be associated with SARS-CoV-2 infection, leading to adjustments in screening and control policies. To avoid the risk of virus spread, all potentially exposed subjects are required to be isolated for 14 days, which is the longest predicted incubation time. However, based on our analysis of a larger dataset available so far, we find there is no observable difference between the incubation time for SARS-CoV-2, severe acute respiratory syndrome coronavirus (SARS-CoV), and middle east respiratory syndrome coronavirus (MERS-CoV), highlighting the need for larger and well-annotated datasets.
Project description:Hand, foot and mouth disease (HFMD) is a childhood disease causing large outbreaks frequently in Asia and occasionally in Europe and the US. The incubation period of HFMD was typically described as about 3-7 days but empirical evidence is lacking. In this study, we estimated the incubation period of HFMD from school outbreaks in Hong Kong, utilizing information on symptom onset and sick absence dates of students diagnosed with HFMD. A total of 99 HFMD cases from 12 schools were selected for analysis. We fitted parametric models accounting for interval censoring. Based on the best-fitted distributions, the estimated median incubation periods were 4.4 (95% CI 3.8-5.1) days, 4.7 (95% CI 4.5-5.1) days and 5.7 (95% CI 4.6-7.0) days for children in kindergartens, primary schools and secondary schools respectively. From the fitted distribution, the estimated incubation periods can be longer than 10 days for 8.8% and 23.2% of the HFMD cases in kindergarten and secondary schools respectively. Our results show that the incubation period of HFMD for secondary schools students can be longer than the ranges commonly described. An extended period of enhanced personal hygiene practice and disinfection of the environment may be needed to control outbreaks.
Project description:We collected environmental surface samples prior to and after disinfection of a quarantine room to evaluate the stability of SARS-CoV-2 during the incubation period of an imported case traveling to Qingdao, China. Overall, 11 of 23 (47.8%) of the first batch of environmental surface samples (within 4 h after case confirmation) were tested positive for SARS-CoV-2. Whereas only 2 of 23 (8.7%) of the second batch of environmental samples (after first disinfection) were tested positive for SARS-CoV-2. The majority of samples from the bedroom (70%) were positive for SARS-CoV-2, followed by 50% of samples from the bathroom and that of 33% from the corridor. The inner walls of toilet bowl and sewer inlet were the most contaminated sites with the highest viral loads. SARS-CoV-2 was widely distributed on object surfaces in a quarantine room of a later diagnosed COVID-19 case during the incubation period. Proper disinfection is crucial to minimize community transmission of this highly contagious virus.
Project description:Since the coronavirus disease 2019 (COVID-19) identified in Wuhan, Hubei, China in December 2019, it has been characterized as a pandemic by World Health Organization (WHO). It was reported that asymptomatic persons are potential sources of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We present an outbreak among health-care workers incited by a doctor who cared a patient with COVID-19 in a Hospital in Wuhan, Hubei, China, which indicates existence of super-spreader even during incubation period.
Project description:Toscana virus (TOSV) is an emerging pathogen in the Mediterranean area and is neuroinvasive in its most severe form. Basic knowledge on TOSV biology is limited. We conducted a systematic review on travel-related infections to estimate the TOSV incubation period. We estimated the incubation period at 12.1 days.
Project description:Information about the Zika virus disease incubation period can help identify risk periods and local virus transmission. In 2015-2016, data from 197 symptomatic travelers with recent Zika virus infection indicated an estimated incubation period of 3-14 days. For symptomatic persons with symptoms >2 weeks after travel, transmission might be not travel associated.
Project description:Knowledge of the incubation period of infectious diseases (time between host infection and expression of disease symptoms) is crucial to our epidemiological understanding and the design of appropriate prevention and control policies. Plant diseases cause substantial damage to agricultural and arboricultural systems, but there is still very little information about how the incubation period varies within host populations. In this paper, we focus on the incubation period of soilborne plant pathogens, which are difficult to detect as they spread and infect the hosts underground and above-ground symptoms occur considerably later. We conducted experiments on Rhizoctonia solani in sugar beet, as an example patho-system, and used modelling approaches to estimate the incubation period distribution and demonstrate the impact of differing estimations on our epidemiological understanding of plant diseases. We present measurements of the incubation period obtained in field conditions, fit alternative probability models to the data, and show that the incubation period distribution changes with host age. By simulating spatially-explicit epidemiological models with different incubation-period distributions, we study the conditions for a significant time lag between epidemics of cryptic infection and the associated epidemics of symptomatic disease. We examine the sensitivity of this lag to differing distributional assumptions about the incubation period (i.e. exponential versus Gamma). We demonstrate that accurate information about the incubation period distribution of a pathosystem can be critical in assessing the true scale of pathogen invasion behind early disease symptoms in the field; likewise, it can be central to model-based prediction of epidemic risk and evaluation of disease management strategies. Our results highlight that reliance on observation of disease symptoms can cause significant delay in detection of soil-borne pathogen epidemics and mislead practitioners and epidemiologists about the timing, extent, and viability of disease control measures for limiting economic loss.
Project description:Fitness costs of incubation ensue whenever the trade-off between incubation and foraging leads to suboptimal incubation or decreased parental body condition. We examined the costs of incubation in a wild population of house wrens, Troglodytes aedon, by experimentally extending or decreasing the incubation period by cross-fostering eggs between nests at different stages of incubation (eggs from control nests were cross-fostered at the same stage of incubation). We determined whether parents or offspring bear the costs of incubation by measuring effects on females and offspring within the same breeding season during which the manipulation occurred, but also by evaluating potential trade-offs between current and future reproduction by monitoring return rates of experimental females and recruitment rates of offspring in subsequent breeding seasons. There was no difference in hatching or fledging success across treatments. There was also no effect of incubation duration on female size-corrected mass, and females from different treatments were equally likely to produce a second brood. Nestlings produced by females did not differ in body mass, tarsus length or residual mass. Neither return rates of females, nor the number of offspring recruited, differed across treatments. We conclude, therefore, that although prolonged incubation entails increased energy expenditures, females are able to offset these losses while foraging, thereby mitigating the costs of incubation. This resiliency is more likely to be seen in income breeders, such as house wrens, that retain some ability to recoup energy expended in incubation, than in capital breeders that are constrained by stored energy reserves.
Project description:The time it takes for malaria parasites to develop within a mosquito, and become transmissible, is known as the extrinsic incubation period, or EIP. EIP is a key parameter influencing transmission intensity as it combines with mosquito mortality rate and competence to determine the number of mosquitoes that ultimately become infectious. In spite of its epidemiological significance, data on EIP are scant. Current approaches to estimate EIP are largely based on temperature-dependent models developed from data collected on parasite development within a single mosquito species in the 1930s. These models assume that the only factor affecting EIP is mean environmental temperature. Here, we review evidence to suggest that in addition to mean temperature, EIP is likely influenced by genetic diversity of the vector, diversity of the parasite, and variation in a range of biotic and abiotic factors that affect mosquito condition. We further demonstrate that the classic approach of measuring EIP as the time at which mosquitoes first become infectious likely misrepresents EIP for a mosquito population. We argue for a better understanding of EIP to improve models of transmission, refine predictions of the possible impacts of climate change, and determine the potential evolutionary responses of malaria parasites to current and future mosquito control tools.