Malaria is a deadly disease that affects many people worldwide. The disease is caused by the parasite, Plasmodium Falciparum, which is transmitted to humans through night-biting Anopheles mosquitos. Although there are medications to treat malaria, the drugs are usually not an effective treatment against the parasite. Furthermore, the illness is most prevalent in less developed countries, such as in the region of Sub-Saharan Africa, where access to treatment is not available. Moreover, “60% of malaria deaths worldwide occur in the poorest 20% of the population (Source 2).” With no access to hospitalization, many residents of malaria-prone regions, especially children and pregnant women, suffer from the severe symptoms, like fever, seizures, bleeding, etc. Additionally, malaria has placed huge financial burdens on the families and governments of these countries (Source 2). Ultimately, malaria is a deadly disease. Since curing malaria is a difficult task, prevention is of the utmost importance.
This article, “Mapping Road Network Communities for Guiding Disease Surveillance and Control Strategies”, utilizes mapping road networks and malaria prevalence to understand the effect of connectivity on transmission of the disease. The data allows for the implementation of effective prevention treatment in localized regions. For example, vaccines could be administered to the population of an isolated region; meanwhile, people affected in high density and high connectivity regions could relocate to less denser areas for protection. The data allows for proper treatment, thus promoting a healthier population that allows individuals greater freedom to live a more fulfilling, longer life. The health care and malaria prevention that could result from these data can be considered one of Sen’s instrumental freedoms, specifically a social opportunity. Instrumental freedoms are the means behind many of the development goals that societies strive towards. Social opportunities encompass the access individuals have to education and health care. Furthermore, Sen mentions that greater health expands other instrumental freedoms such as political freedoms, as it enables greater political participation. The authors of this article primarily deal with the human development dimension of life expectancy. This is because malaria data can bring about powerful treatment plans that can decrease the mortality rate, leading to a longer life. The sustainable development goal that is focused on is good health, and well-being. The authors utilize geospatial datasets of road networks in West Africa, which includes information like the density, pattern, and amount of road within a region. They combine these data with population data and malaria prevalence information. From these data, they are able to create a color-coded map that displays a weighted African road network, with malaria prevalence multiplied by population density layered on top. This was accomplished thorough raster files with 5 x 5 grid cells of malaria prevalence. On top, higher resolution of 1 x 1 grid cells were placed that represented population density. Lastly, the African road network was placed on top. From this map, one is able to recognize the regions that require immediate attention: the most connected, dense, and malaria prevalent locations. GPS navigation and cartography were used to collect data on the African road network (ARN) while data on malaria prevalence was obtained from the Malaria Atlas Project. When modeling the ARN, roads are represented by links and road junctions are nodes. In order to create the primal road network, an algorithm was used to determine the longest road. From there, the algorithm searches for an edge to determine the second longest road. Any interceptions become connected, and the algorithm continues onward. After the road network has been made, a modularity optimization algorithm is able to detect communities within the network. The communities mapped were cross-checked with another study that was centered on communication detection based on migration data. Finally, to integrate disease prevalence, mathematical functions were employed to calculate the maximum numbers of infections of each road segment. Ultimately, the authors are investigating how today’s increasing interconnectedness contributes to the spread of disease. They are showing that although interconnectedness is beneficial for development as it allows for the exchange of information, ideas, goods/services, it also can be negative as it leads to greater spread of very infectious diseases. One drawback of this study was that it did not distinguish between the hierarchy of roads. For example, from the maps created, it would be impossible to tell whether a road is a highway or simply a smaller neighborhood road. This is important because highways tend to carry more of the population, resulting in greater transmission rates. Ultimately, these authors are investigating how road network connectivity could provide useful information about malaria transmission. The algorithms, GPS navigation, and technology used are of the utmost importance to combat malaria. The article says that, “An understanding of how areas are connected, the strength of these connections and how this translates into disease spread is valuable for planning surveillance and designing control and elimination strategies.”
This article, “Malaria”, provides valuable information about the illness, covering topics such as transmission, epidemiology, laboratory discoveries, treatments, and prevention. Suh, Kain, and Keystone (2004) state that about 300-500 million cases of malaria occur each year, including 1.5-2.7 million deaths. From the statistics, it’s clear that malaria is a dangerous disease that significantly affects people worldwide. It can most commonly be found in lesser developed regions, specifically sub-Saharan Africa, and affects children, pregnant women, travelers, and the poor disproportionately. Moreover, in some countries like Canada, malaria is an imported disease. The map indicates that places like Central America, South America, south Asia, and the middle east are affected. Aside from the severe health risks, it also poses as a social and economic burden to affected people. Large financial costs result on people and governments. It is a parasitic infection that is passed on through the bite of a mosquito - Plasmodium Falciparum transmitted through the nighttime bites of Anopheles mosquitos. Most travelers aren’t aware that they may have malaria, as symptoms may take weeks to months to present themselves. Symptoms include respiratory distress, seizures, abnormal bleeding, high fever, jaundice, etc. Infection by P. Falciparum requires hospitalization and treatment by the drug, chloroquine. In many malaria-prone areas, insecticide-treated bed nets have significantly reduced the infection rate. However, drug cost and delivery still remain an issue in lesser-developed countries. At the time this article was published, there was no vaccine available. Today, a vaccine may be administered- however, only with an extremely low efficacy. The main protection against malaria must be simple prevention.
At the time this article was published in 2014, it was understood that in order to fully achieve elimination, countries must move away from universal prevention actions to focus more on targeted treatment attacks. The authors argued that infection prevalence is a poor metric to combat the disease, as it is not specific enough to local regions. They argue that a surveillance system is required – one that would utilize malaria transmission information to design proper elimination practices. For example, the Haitian government attempted to eliminate malaria in the 1960s through mass drug administration and DDT- spraying. However, this time-consuming and costly treatment plan failed since the mobile population kept introducing the parasite into regions that had just been cleared. Thus, knowledge of human movement within a malaria-endemic country can be useful for identifying the best plan of action to avoid resurgence of the parasite. This study analyzed billions of call detail records (CDRs), which provided data on human movement between regions in Namibia, a malaria-endemic country in Africa, to map “source” and “sink” areas (net infection export/import locations). CDRs were provided by a leading mobile phone service provider in Namibia, Mobile Telecommunications Limited (MTC), for a 12-month period. These data were used along with a modularity optimization algorithm that allowed for community detection within regions, and thus communities were identified that are strongly connected by human and parasite movements or are isolated. It was found that movements follow major transport networks, with the largest amount of movements in Northern Namibia. It also represents one of the largest “source” regions of the parasite. Finally, a risk zone map was created to identify where interventions should be targeted, with Northern Namibia as zone 1. In brief, the approach of high-resolution mapping combined with CDR is very useful to deploy limited resources in an effective way. High transmission “sink” regions, bed net use could be encouraged, while high transmission “source” regions are target with drug administration and DDT-spraying. The careful targeting of malaria source regions relates to Amartya Sen’s definition of human development because it enhances people’s everyday freedoms. Avoiding resurgence of Plasmodium Falciparum in clear, treated regions leads to greater health of individuals, allowing for more social and economic opportunities. Specific treatments to each region instead of universal prevention methods all over will result in fewer financial costs, allowing for greater economic freedom. The authors are focused on addressing the human development dimension of life expectancy, as malaria infection is deadly. The SDG focused on is good health and well-being. The study described within this article really encompasses the “leave no one behind” mentality. Geospatial datasets used include health districts of Namibia, CDR data, and various spatial covariate datasets representing rainfall, temperature, elevation, distance to water, etc. Some geospatial data science methods include using the algorithm “Random Forest” to model the risk of different cases, constructing a weighted network of movements with CDR data, and using community detection algorithms. The authors are ultimately investigating how a highly mobile population can affect the spread of the parasite that causes malaria to understand how human movement affects epidemiology. The authors sought to answer how best to treat different malaria-endemic regions based on patterns of human movement.
A highly effective method of vector control that was proposed in this article is improvements to housing in malaria-endemic regions. Modern housing provides better production against malaria compared to traditional housing through 2 mechanisms: tiled or metal roofs provide a physical barrier to mosquitos instead of thatch, and indoor daytime temperature is higher for metal-roofed houses than thatch-roofed houses, which impairs parasite development if it exceeds the optimal temperature. The article mentions that there is not much data on how housing in Sub-Saharan Africa may affect malaria transmission. In this study, DHS and MIS data were utilized to test the hypothesis that modern housing will lead to lower malaria infection rates in children in Sub-Saharan Africa. DHS, demographic and health surveys, and MIS, malaria indicator surveys, are administered every 5 years in target countries. The malaria infection prevalence was determined through microscopy tests of blood samples and through rapid diagnostic tests. The surveys classify houses as either “modern” or “traditional”. Modern houses have finished roofs, walls, and floors, and are typically built with cement, wood planks, etc. All other houses are classified as traditional. However, for the purpose of this study, wood planks were considered to be a traditional material, as they have openings that are permeable to mosquitos. For each survey, the study modeled the association between housing quality and malaria infection in children aged 0-5 using conditional logistic regression. It was found that residents of modern houses had 47% lower odds of malaria infection, and “45%-65% lower odds of clinical malaria compared to residents of traditional houses.” The study has a couple limitations including controlling for confounding variables. One of these include that wealthier families may have fans within their modern homes, which further reduces mosquito survival. Overall, housing quality is an important risk factor to consider. Additionally, the study also compared the association between housing quality and malaria to the association between insecticide-treated nets and malaria. It was found that children sleeping under nets had “15% to 16% lower odds of malaria infection than those not reported to have done so.” Similarly, residents of better housing quality had 9% to 14% lower odds of malaria inflection. From the data, it can be observed that poor housing quality is, in fact, an important risk factor. This study relates to Amartya Sen’s definition of development as better health grants individuals more social and economic opportunities, but also because it is concerned with social values including family care. The dimension of life expectancy and the SDG of good health and well-being are being focused on. A cross sectional data analysis is done on data sets including malaria infection, housing, insecticide-treated net use, etc. Conditional logistic regression and chi-square tests were used. Ultimately, the authors are investigating the importance of architecture in aiding in prevention against vector-borne diseases. They are investigating the human pattern of settlement and how it relates to disease prevention. The authors sought to answer whether improving houses would lower the risk of malaria infection, thus helping malaria-endemic countries become one step closer to eliminating the disease.
This study is concerned with the treatment of malaria in its last phases. For example, countries like Swaziland, located in Sub-Saharan Africa, are on the verge of malaria elimination but require accurate measures of progress and meaningful metrics that will allow them to direct intervention in areas that need it the most. Thus, Swaziland needs to identify the regions where the malaria parasites are still spreading throughout the population, so it can target those communities. In order to accomplish this, researchers can look at past malaria data and calculate how many infections are caused by each new case. Three important metrics include: vulnerability (rate of malaria importation), receptivity (potential for ongoing local transmission), and malariogenic potential (the expected number of cases that could occur due to vulnerability and receptivity; the product). To calculate receptivity, computational methods were used that included estimating links between cases and labeling some cases as “parents.” Local cases were linked to a “parent” case, and a weighted network was created. These data come from surveys Swaziland conducted, collecting information about demographics, household location, malaria prevention measures, symptoms, dates of treatment, etc. Additionally, to calculate receptivity, data scientists needed information regarding the number of direct offspring at each case location. Spatial covariates were used, which included weather, geography, population density, and urbanicity. They also collected information like travel history, which proved useful in identifying whether the malaria cases were imported or not, which helped to calculate vulnerability. To further calculate vulnerability, a mathematical function was used that related population density, distance to Mozambique, and distance to roads. A mathematical formula computed “the likelihood that a mosquito infected by a putative index case at a particular point in space and time later infected an individual identified as a locally acquired case at a different point in space and time.” The formula took into account factors like mosquito lifespan, mosquito movement, human movement, and malaria epidemiology. Together, the vulnerability data and receptivity data were used to provide information on malariogenic potential. The resulting map showed where “locally acquired/transmitted cases are most likely to occur, and thus where resources may need to be prioritized to prevent reestablishment of malaria.” This research article was very interesting as it expands people’s freedoms because healthier individuals have more social and economic freedoms, but also because it ensures greater future stability of freedoms. In contrast to other prevention methods, this data science method maps out the potential of infection of malaria. The dimension of life expectancy and the SDG of good health and well-being are being addressed by the authors. Ultimately, the authors are focused on investigating how the reproduction rate of this parasite and its past history could reveal information about possible future infections. The authors sought to find a proper metric that would give countries in their final stages of malaria elimination information about where to direct their interventions and treatment.