In this task, you're given a paragraph and title from the research paper. Your task is to classify whether the given title is suitable or not for the research paper based on the given paragraph. Return "True" if title is proper according to paragraph else "False".

Paragraph: To evaluate the aetiological agents and epidemiologic features of severe fever with thrombocytopenia syndrome (SFTS) in Hubei province, China, sera from patients were collected from January to December 2011. All cases occurred from April to December, and the epidemic peaked from May to August. The ages of patients ranged from 10 to 86 years (median = 55 years), and the incidence of SFTS increased with age. The female:male ratio of cases was 1.008:1, and 54.6% (77/141) and 1.4% (2/141) of the cases were confirmed by qPCR to be SFTSV and Hantavirus (HV) infection, respectively. No case of simultaneous infection with two or more pathogens was found. The research in this paper showed that some suspected SFTS cases are confused with HV infection due to similar symptoms. The analysis showed that the distribution of SFTSV has a marked regional aggregation in Hubei province. 
 Title: Pathogens and epidemiologic feature of severe fever with thrombocytopenia syndrome in Hubei province, China
False

Paragraph: The World Health Organization (WHO) and other organizations report that the prevalence of human diseases during the past decade is rapidly increasing. Population growth and the pollution of water, air, and soil are contributing to the increasing number of human diseases worldwide. Currently an estimated 40% of world deaths are due to environmental degradation. The ecology of increasing diseases has complex factors of environmental degradation, population growth, and the current malnutrition of about 3.7 billion people in the world. 
 Title: Ecology of Increasing Diseases: Population Growth and Environmental Degradation
True

Paragraph: The spatial and spatio-temporal scan statistics proposed by Kulldorff have been applied to a number of geographical disease cluster detection problems. As the shape of the scanning window used in these methods is circular or elliptic, they cannot find irregularly shaped clusters, say clusters occurring along river valleys or in cases where disease transmission is linked to the road network. In this study, we propose a more flexible geometric structure to be used as a spatial or spatio-temporal scanning window. A particle swarm optimization (PSO) is used to optimize the scanning window to determine disease clusters. We evaluated the proposed method over a number of spatial and spatio-temporal datasets (Breast cancer mortality in Northeastern US 1988-1992 and different types of cancer in New Mexico 1982 -2007 . Experimental results demonstrate that the introduced approach surpasses the results produced by the circular and elliptic scan statistics in terms of efficiency, especially when dealing with irregularly shaped clusters. 
 Title: Regular paper A new PSO-optimized geometry of spatial and spatio-temporal scan statistics for disease outbreak detection

True