1887

Abstract

Common cold is a viral infection of the upper respiratory tract that brings discomfort to people for few days to few weeks. Although, seasonal common cold is widespread during wintertime, the disease can be contracted all year round. Initially common cold was believed to be link to exposure to cold air because of its widespread during that period; the disease has been categorized as infectious disease there after (William and Sheldon, 2009). Common cold can bring about serious economic hardship or downfall to workforce because it often results in absenteeism from work or school (Babak et al., 2009). The disease is the most commonly encountered infectious disease in human and most frequent illness managed by general practitioner. It was reported that about 25 million people visit doctors yearly in the USA alone with common cold (Heikkinen and Jarvinen, 2003). Common cold can be caused by a variety of virus that depends on a number of factors such season and age, with rhinoviruses been the most common cause of common cold. Heikkinen and Jarvinen, 2003 provide extensive literature on common cold that include causes, epidemiology, clinical diagnosis, treatment and prevention. The effect of weather conditions is very crucial in infectious disease modeling. Social network contacts are vital when seeking to understand and predict the spread of infectious diseases inhuman populations. The transmission of infectious disease has been linked to social contact behavior (Willem et al., 2012, Wallinga et al., 2006) and animal contacts (Kifle et al., 2015, Jones et al., 2008). The frequency of infection may depend on the number of social and animal contacts. The spread of infectious disease can be control through understanding the dynamics of social contact behavior and animal contact. It is reported that adults get the illness two to three times in a year while children are infected five to seven times a year (Babak Amra et al., 2009, Heikkinen and Jarvinen, 2003) The objectives of this project are: a) To explore the dynamics of transmission of the common cold between different age groups. b) To investigate the effect of social contact patterns on the disease incidence. c) To estimate the effects of other associated risk factors such climatic and environmental variables. d) To investigate the effect of socioeconomic background such as family size, education, contact type and so on. To explore the influence of animal ownership on the frequency of the infectiousness. Conceptual Framework and data collection This study will look at the relationship between common cold and climatic changes with the effects of social contact through the development of flexible predictor statistical models. We shall combine flexible statistical models for network data to study these relationships. The data sets will be collected by the undergraduate students over a period of 3-4 months within Qatar through the use of surveys. The social network contact survey which includes the illness status and some demographic variables will be conducted within and outside Qatar University. Recruited participants will cut across different age groups, nationality and gender. An adapted version of the social contact survey POLYMOD (Improving Public Health Policy in Europe through the Modelling and Economic Evaluation of Interventions for the Control of Infectious Diseases) will be used for contact diaries. The participants will be asked if they could be contact again via email or telephone (in a month or so) for completion of the second diary. Firstly, they will be asked to answer few demographic questions such as, the number of family member, age, gender, country and educational attainment. Secondly, for each participant, the daily number of social contacts will be recorded as well as contact type. Participants will be asked if they engaged in a direct conversation with someone else at most three meters away or touched someone else (e.g. shaking hands or kisses on the chick), this was considered as a “physical” contact, even if not a word was spoken. In additional to social network contact, participants will be asked about their interaction with animal. Ownership of animal which is defined as having at least one live animal in the household in which the participant was spending the majority of his/her time. Animals with be categorized into four classes: pets (cat, dog, fish), livestock (horse, sheep, camel, cow), poultry (chicken, turkey, pigeon) and “other”. And lastly, each participant will be asked about his/her illness status will be asked (such as onset date and severity). The climatic data sets that will be used are mean daily temperature, humidity and dust aerosol. The collection of the daily climatic data will commence at least a weeks before the survey. Few weeks later (between 2-4 weeks), participants will be contacted again via email or telephone with similar follow-up questionnaire for the second social contact diary. Modeling techniques Logistic regression is a technique used for making predictions when the dependent variable is a dichotomy, and the independent variables are continuous and/or discrete. For analysis of social network data (clustered), random effect term will be added to the regression model to account for the correlation in the data. The resulting model is a mixed model including the usual fixed effects for the regressors plus the random effects in the predictor. Development of generalized linear mixed models (GLMM) for dichotomous data has been an active area of statistical research. Several approaches, usually adopting a logistic or probit regression model and various methods for incorporating and estimating the influence of the random effects, have been developed. The mixed-effects logistic regression model is a common choice for analysis of correlated dichotomous data and is arguably the most popular GLMM.

References

Babak A, Hamid S, Shahin S, Mohammad G. (2006) Prevalence of the Common Cold Symptoms and Associated Risk Factors in a Large Population Study. Tanaffos 5(3): 13-17.

Heikkinen T, Jarvinen A. (2003) The common cold. The Lancet, 361 (9351): 51-59.

Jones K, Patel N, Levy M, Storeygard A, Balk D, Gittleman J, et al. (2008) Global trends in emerging infectious diseases. Nature 451: 990-993. doi: 10.1038/nature06536 PMID: 18288193.

Kifle YW, Goeyvaerts N, Van Kerckhove K, Willem L, Faes C, Leirs H, et al. (2015) Animal Ownership and Touching Enrich the Context of Social Contacts Relevant to the Spread of Human Infectious Diseases. PLoS ONE 10(7): doi:10.1371/ journal.pone.0133461.

Wallinga J, Teunis P, Kretzschmar M. (2006) Using data on social contacts to estimate age-specific transmission parameters for respiratory-spread infectious agents. American Journal of Epidemiology 164: 936-944.

Willem L, Van Kerckhove K, Chao DL, Hens N, Beutels P. (2012) A Nice Day for an Infection? Weather Conditions and Social Contact Patterns Relevant to Influenza Transmission. PLoS ONE 7(11): doi:10.1371/journal.pone.0048695.

Loading

Article metrics loading...

/content/papers/10.5339/qfarc.2016.HBSP1767
2016-03-21
2019-09-20
Loading full text...

Full text loading...

http://instance.metastore.ingenta.com/content/papers/10.5339/qfarc.2016.HBSP1767
Loading
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error