Review of Populations-Based Study of Chlamydial Infection in China; A hidden Epidemic

Throughout this article, it talks a lot about the contact rate that individuals have in a bigger population. This doesn’t necessarily tie into our general epidemics that we think about or are considering to write about in our paper, but it does relate the population density to the rate of an epidemic. This also touches on the topic of the behavioral patterns of the population as a whole as well. This may be a different way to measure an epidemic and this disease in particular is spread a different way than other epidemics. This can still be used to connect relationships with people and behavioral patterns that could increase their exposure rate. They took a sample of 3426 people between the ages of 20-64, whom of which had to provide a urine sample of which only 69% provided.

Though many refused to provide a urine sample, and many failed to complete the interview, they still recorded some decent results. They did find that this particular epidemic has a higher rate in more wealthy men. This could be because they can afford possible commercial sex networks. Within these sex networks they find that men are a higher carrier of this type of disease. This again falls back to the behavioral patterns of the individuals. Since these people working for the sex networkings are being exposed/ exposing themselves to others interested in this type of contact, these are the people that are effected immediately. These people then may go sleep with someone who is not in the commercial sex industry and spreading this disease to them. Since this industry is such a large industry in China and these exposure rates may be higher in more populated areas, this increases the exposure rate to millions vs. those that are only exposed to thousands. Again, supporting our theory that an epidemic rate is increased within the higher populated countries.


Modeling the effect of transient populations on epidemics in Washington DC

This article touches on the effects on disease spread in a subpopulation. They evaluate the types of effects that transients may have on epidemics. They took a synthetic population from Washington DC. This included people anywhere from leisure travelers to business travelers. What is a synthetic population and how might it be obtained you may ask. Well, a synthetic population can be obtained by pulling together many different data sources. This will build a very detailed simulation of a specific type of population interaction and the types of contact network those in that population may encounter on a daily basis. Populations and the different types of population mixing patterns can have a large impact on the effect of an epidemic rate. Simulations they used without transient allowed them to quantify the difference an epidemic rate and other contributing characteristics, like the attack rate, and day of the epidemics peak in the curve that would be due to the transients. Transients sharing can actually have a significant impact on these subpopulations and can act as a type of reservoir to these susceptible people. Keep in mind that although it is a subpopulation and there may be a lot of contact between the same people although they do not know it, there can also be exposure from other subpopulations as well. This will introduce a possible new strain of the virus.

What this study did was take a simulation of an influenza type illness. Since, in a bigger city like DC, there is going to be a high contact rate with the different types of population mixing happening due to tourism and residents that live in the area. Since the highest contact rate would be at more touristic places, they decided to study in the tourists areas such as museums. Instead of closing down the museums they just encouraged healthy behaviors such as using had sanitizer or washing hands. Since closing down a museum wouldn’t do it any good, and by promoting healthy behaviors this can help either reduce or delay the peak of an epidemic. Since some of these tourist destinations attract a significant number of people on a daily basis, they targeted interventions and either closed major destinations or set up healthy behavior interventions, promoting sanitary behaviors. They found closing down these places didn’t do any good to help the spread of disease. They did, however, find that the exposure to promotion of healthy behavior did provide a fairly low efficiency rate. They came to the conclusion that the number of transients significantly impact the number of infections. They assumed that closing did not work because this would just redirect the tourists to other places still increasing the contact rate. They came to the conclusion that promoting healthy behaviors, in the long run, could significantly reduce the rate of an epidemic. This did highly depend on the efficiency of the intervention, which would lead to aiding in the reduction of transmission within the locations where it was promoted.

Finding an Exposure Relevant Metric for Population Density

While reading this article, it was very easy to understand the fact that population density did indeed have a huge impact on exposure rates. It talks about those who live in a higher density populated area, they are more exposed to pollutants of human activity. These rates don’t specifically talk about disease exposure but also take into consideration of the pollutants that the humans produce and how we are exposed to them. They can range anywhere from driving, to heating, to waste. After the exposure to pollutants and potential disease this can lower our immunity, and the diseases can take affect faster. They decided to test their theory in an area in Finland. They took a higher density population and compared it to the same population but the density was more spread out. They used graphs to show us what they had seen. In the first figure they show, the radius is 1km. It shows the percentage of the population that is affected and the number of people affected. You see that is spreads rapidly. The next graph is that of a 10km radius with the same population that is in the 1km radius. The graph is not as smooth as it increases there appear to be some spikes and some small drops. The curve is a little more rough. The rate seems to be a lot less steep. It clearly affects people more in the higher density population because they have a higher exposure rate. There seems to be a higher population percentage affected in the same period of time they recorded for the 10km radius. They give a graph of the densities they took in multiple areas of Finland just giving us a general idea of where they got all of their statistics. This paper does an excellent job of showing and supporting our paper and our results of the relationship between population density and the widespread of an epidemic occurrence.


Website link:

The Effects of Population Density on the Spread of Disease

This article talks about the effects the population density may have on the spears of a disease. They decided to run a simulation while using measles as the disease. The goal of this paper was to identify if there was a relationship between population stages and the spread of disease in a local population. They had two candidates initially. There were those that were susceptible, non infected, and the infectives, those who were infected with the disease. Once a susceptible came into contact with an infective, started developing some signs and symptoms, they would become known as an infective as well. As time went on, these trials per density, were run 1000 times each. As the generations moved on, they would add new invectives, seeing if that would recreate the infected rates. The only thing that would change this time would be that there were still “survivors” in the next generations, or trials. If you were to look at one of your graphs you would see up until about generation 4 the disease was spreading drastically. Once the generation hit 4, it dropped of drastically and there appeared to be a lot less infected as time went on from that point as well. They believed that that had to do something with building immunity. Those who were no longer affected as time went on were known as the uneffectives. The data ended up showing that those with higher population densities had a significantly higher infectious rate than those with a lower density. This simulation did end up proving that the density does have a significant effect on how quickly diseases spread. This all comes down to contact rate. The higher the population density, the higher exposure you have to the potential disease ridden people and objects. I really enjoyed reading this article and thought that it included a lot of useful information for our project. It was clear and easy to understand, and it made sense that you’d have an increased rate with a higher population. The graphs were easy to read and understand and their data supported their results.

How Does Transmission of Infection Depend on Population Size (review)

This article talks about the effect the population size has on the infection rate. This article states that people that tend to live in a higher populated areas are more likely to come into contact with the “infection” that is going around. One example they use to support this is that if you were to take a population of 100,000 people vs. a population of 1,000,000 people, those living with the 1,000,000 are going to be 10x more likely to come into contact with the “infection” than those living in the 100,000 population group. This would make sense due to potentially an individual being in closer facility with others say, during public transportation, public offices, or even at entertainment events. The more populated a city is, the more likely items and/or people experience exposure to the diseases. These scientists were able to create different groups with different populations and infect maybe 1% of the population. With that they then were able to see the growth rate of the infection over time.


They took two groups of populations with the same density.  They came up with 3 different equations.

dX/dt=A-bX-βXY+ϒZ, dY/dt=βXY-(b+α+ν)Y, and dZ/dt=νY-(b+ϒ)Z.

“A is the number of individuals added per unit of time, b is the probability per unit of time to die from causes unrelated to the infection, β is the transmission constant, ϒ is the probability per unit of time losing immunity, αis the additional mortality due to the infection and v is the probability per unit of time that the individual recovers”(Jong).

They used mice for this experiment. They used two different groups of mice. One with a lower population over all as well as a lower population density. The second group was a higher density, higher population. They infected only a few mice and watched how quickly the “infection” spread.  Overall they did have the results showed that their theory was correct. The population size and density did indeed have a positive correlation with the amount infected and how quickly it was to spread. However, this hypothesis was only in the mice. When they compared it to actual data with human communities. Therefore, the experiment and the hypothesis for the experiment with the mice were correct, just not in an actual community. When they compared their results to the results in a community, the estimate they had was 100,000 times less than expected per household.

Link to article: