Veterinary and Zoonotic Infectious Diseases Assignment Answers

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Question 1. An unidentified viral disease in St Kilda Field mice

This question explores a viral disease in the St Kilda Field mice, focusing on how it spreads within an isolated population. It covers infection dynamics, R₀ calculations, herd immunity, and the SI model for disease transmission

Veterinary and Zoonotic Infectious Diseases Assignment Answers

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Richard White 8 Years | PhD

(a) Meaning of R₀ and Herd Immunity

A basic reproduction number is the R₀, which measures how contagious an infectious disease is (Delamater et al., 2019). More specifically, it informs us how many other animals, on average, another animal inoculated with that infection will transfer the disease to within a population where everyone is not immune.

For instance, if R₀ is 2, that implies that one infected St Kilda Field mouse will continue to infect two more mice, and each of these two more will infect two more, resulting in exponential spread (Ramirez, 2020). Concepts like this are often used in assignments writing help to demonstrate calculations of disease transmission and herd immunity.

Herd immunisation protects a populace when a sizable piece is invulnerable because of inoculation or past contamination (Bullen, Heriot and Jamrozik, 2023). This principle is central in Veterinary and Zoonotic Infectious Diseases, as it helps prevent the disease from spreading swiftly, even among unvaccinated individuals. This helps prevent the disease from spreading swiftly, as relatively few hosts are susceptible to it. However, in this case, it is also suitable for unvaccinated individuals because the infection is more complicated to spread.

(b) Vaccine Coverage Calculation

To prevent the spread of the disease, calculate the proportion of the population that needs to be vaccinated using the formula:

P(vac) = 1 – 1/R₀

Given:

R₀ = 2

P(vacc) = 1 – (1/2) = 0.5

The mouse population must be vaccinated so that 50 per cent can end the disease spreading.

The population of St Kilda Field mice is 1,500.

Number of mice to vaccinate is 0.5 × 1,500 = 750 mice.

The answer is 750 St Kilda Field mice would have to be vaccinated to be protected against the colony.

(c) SI Model Timeline

We use the SI model, which assumes that once infected, mice stay infected.

Formula: I(t+1) = I(t) + β × S(t) × I(t)

Where:

I(t) = number of infected at time t

S(t) = number of susceptible at time t

β = 0.1 (transmission rate per day

Starting values:

Day 0: I = 1, S = 1,499

(d) Discussion of the SI Model and Limitations

The susceptible-infected (SI) model is one of the basic frameworks of epidemiology that models the pathology of the disease (Dhungana and Ghimire, 2021). Its (no other word will suffice) divides the population into two parts: (a) Susceptible (S), (b) Infected (I). This is a premise that makes it assumed that after infection individuals will continue to be infectious for the remainder of the timeline with no recovery or death.

Strengths of the SI Model:

• Easy to apply: It is simple and beneficial early in an outbreak when data is limited.
• Suitable for the worst case: Because the model assumes infecting people is permanent, it gives a high-end approximation of how quickly an infectious disease could spread if not stalled. Such modeling approaches are frequently discussed in Veterinary and Zoonotic Infectious Diseases to understand outbreak dynamics in animal populations.

This model applies to isolated populations, such as St Kilda, with a defined mouse population and no immigration or emigration (Knopoff et al., 2022).

Limitations:

• No recovery or death was included: If a mouse is infected, it will remain infectious forever. This is unrealistic, particularly if the disease has a finite period of infection or leads to death.
• No latency or incubation period: The SI model doesn’t cover any time delay between exposure to the disease and infection (Knopoff et al., 2022).
• Real-world situations: Individuals may adopt altered behaviour (reduced contact) or develop immunity, particularly when vaccination is introduced.
• Homogeneous mixing assumption: Here, it is assumed that each infected individual has a similar probability of meeting any susceptible individual. On an actual island, geography and behaviour influence contact patterns (Foley et al., 2023).

Implications for St Kilda vs Mainland: The model is more accurate on St Kilda because of a minor, isolated population, a known population size, and the birds' limited movement. Homogenous mixing may approximate reality rather well, provided that mice interact closely with each other (Foley et al., 2023).

The SI model would be less realistic on the mainland, which has large populations, complex habitats, and variable population densities. All of this complicates the picture. Transmission is not just movement between regions but between regions. Furthermore, there are varying contact rates, and sometimes predators can be present (Itescu et al., 2019). In such a context, models such as SIR (Susceptible-Infected-Recovered) or SEIR (additional exposed compartment) would provide better predictive accuracy.

Question 2: Rodent GCD Cases and Reservoirs

This question examines the relationship between rodent populations and human GCD cases. It identifies which species act as reservoirs and analyzes how fluctuations in rodent abundance influence disease transmission.

(a) Present the data in Table 1 as a Figure

Figure 1: Rodent Abundance and Human GCD Cases (2001–2020)

This graph provides a visual of rodent abundance (per hectare) of Marsh Rats, Grass Mice, Tuco Tucos, and annual GCD cases from 2001–2020 reported by humans. There are clear trends showing that Marsh Rat abundance correlates most closely with the increase in spikes in human GCD cases, especially in 2006, 2011 and 2017.

(b) Does F. bownii have a rodent reservoir, and if so, which species?

The evidence clearly shows that Francisella brownie has a rodent reservoir and that the Marsh Rat (Holochilus chacarius) is the principal reservoir. This highlights a key topic in Veterinary and Zoonotic Infectious Diseases, demonstrating how animal hosts maintain pathogens that can spill over into humans. The conclusion is derived based on data from Tables 1 & 2 and Figure 1:

Correlation with Human Cases (Table 1 & Graph):

Human cases in the years of GCD closely follow peaks of high Marsh Rat abundance. There is no consistent correlation between Grass Mouse and Tuco Tuco with GCD cases.

Infection Prevalence (Table 2):

Across all years, the infection rates were significantly higher in Marsh Rats.

2020: 74%

2021: 87%

2022: 51%

2023: 60%

In contrast:

Grass Mice: 7–14%

Tuco Tucos: 4–19%

This, however, suggests the much greater responsibility of Marsh Rats in maintaining and disseminating the infection.

Genetic Analysis (Figure 1):

At the bacterial locus doss1, Marsh Rats contained all 11 genetic alleles. A total of six alleles were unique to Marsh Rats and exhibited genetic diversity and long-term association.

Three shared human alleles with Marsh Rats and two found in unrelated rodents suggest that Marsh Rats are likely an important source while other species fill the role of transport host.

(c) Are you surprised 2021 was the most recent peak year for GCD cases?

• Rodent Infection Data (Table 2): During 2021, the infection prevalence of Marsh Rats has never been higher than 87% since we began monitoring (Anstead, 2020). Other years, Grass Mouse (14%) and Tuco Tucos (19%) also had higher amounts.
• Epidemiological Link: Due to the high infection rate among the Marsh Rats, the major reservoirs, zoonotic spillover to humans is significantly increased, especially in deforested areas where humans increase their contact with rats (Tajudeen et al., 2022).
• Deforestation Context: Once deforested, Marsh Rats may become more numerous near farmland and be in contact with people.

(d) Propose three interventions to control GCD within 12 months

Targeted Marsh Rat Control (Rodent Population Management):

• Removing Marsh Rats near human settlements will help lower the risk of human infection from the rats.
• Modify the habitat when needed selectively and trap when selectively required, especially in areas of high rat density.
• Reducing contact with primary reservoirs may reduce contact with an animal and prevent an outbreak (Mummah et al., 2020).

Environmental Hygiene and Rodent-Proofing on Farms:

• Block rodents entering, secure food storage, and remove debris in rodents’ nests.
• Encourage the use of practices that reduce rodent attractants.
• WHO guidelines recommend rodent-borne disease control using community-based hygiene campaigns.

Public Health Surveillance and Education Campaigns:

• Build active disease monitoring and farmer education.
• Teach people about safe farming practices, rodent exposure issues and the use of personal protective equipment (PPE) (Mummah et al., 2020).

Question 3: Malaria Risk and Climate Change

This question investigates how projected climate changes affect malaria risk across different African regions. It highlights the influence of temperature, rainfall, and environmental changes on vector populations and disease spread.

(a) Description of the Figure

The figure depicts projections of the number of human populations at risk of malaria in Central, Eastern, Southern and Western African regions under severe climate change scenarios for 2020, 2030, 2050 and 2080. Although the risk of malaria in Central Africa will remain low at 3.1 million by 2040, it will increase from 2.9 million in 2020 to 106 million in 2030 before declining to 5 million in 2080 (Kovats et al., 2018). Initial warming may give a competitive advantage to malaria transmission, but higher or different temperatures or ecological changes may also reduce mosquito suitability or lead to population displacement.

The population at risk in eastern Africa continues to increase steadily throughout the projection period. From around 30 million people in 2020, the risk population rises to nearly 100 million by 2080 (Kovats et al., 2018).

All decades have had a low and stable population at risk in southern Africa. The cause might include a persistent arid environment, vector lack or effective disease control measures.

The risk population has drastically declined from an estimated 100 million in 2020 down to less than 20 million in 2080 in western Africa (Kovats et al., 2018). In terms of future potential, this reversal implies that the area may become less friendly to malaria vectors as a result of environmental change or higher public health interventions.

(b) Climate Change and Vector-Borne Zoonotic Disease Ecology

Many vector-borne zoonotic diseases are affected by ecological dynamics and transmission potential, which are, in turn, caused by climate change. Rift Valley Fever is an example of a disease that affects humans and livestock and is mosquito-borne—primarily in Africa (Tinto et al., 2023). The disease vectors' habitats will expand, and their geographic locations will change as global temperatures rise and rainfall patterns become more erratic.

Temperature can affect mosquito development and the number of bites, both of which increase the likelihood of disease transmission. In addition, high temperatures can reduce the extrinsic incubation period of the virus within the mosquito so that the vector becomes more quickly infectious (Tinto et al., 2023). Mosquitoes can thrive in regions that used to be too cold or dry for mosquito survival, which could allow diseases like Rift Valley Fever to emerge elsewhere.

(c) Challenges in Disease Surveillance

Surveillance in Human Populations

Given the challenges associated with disease surveillance in human populations, the main hurdle is restricted access to healthcare infrastructure, particularly in rural or underserved areas. Many affected communities may fail to detect, monitor, and respond to disease outbreaks due to the absence of diagnostic facilities, trained medical personnel, or reliable information reporting systems (Chemison et al., 2024).

Also, the under-reporting of diseases is due to social and cultural barriers. People may refrain from seeking medical care in some communities because of stigma, lack of trust or traditional beliefs about illness (Birungi et al., 2021). Due to this, surveillance data are limited in accuracy, and early intervention efforts are slowed down.

Surveillance in Wildlife Populations

In addition, wildlife disease surveillance is far more complex because its hosts are elusive and dispersed far and wide. Some species are difficult to monitor as they are nocturnal, migratory, and found in far away, inaccessible areas (Chemison et al., 2024). Such work needs to be carried out on the field, which is very logistically and resource-intensive.

References

• Anstead, G.M. (2020). History, Rats, Fleas, and Opossums. II. The Decline and Resurgence of Flea-Borne Typhus in the United States, 1945-2019. Tropical Medicine and Infectious Disease, [online] 6(1), p.2. doi:https://doi.org/10.3390/tropicalmed6010002.
• Birungi, D., Aceng, F.L., Bulage, L., Nkonwa, I.H., Mirembe, B.B., Biribawa, C., Okethwangu, D., Opio, N.D., Monje, F., Muwanguzi, D., Ndumu, D.B., Aruho, R., Lumu, P., Lutwama, J., Kwesiga, B. and Ario, A.R. (2021). Sporadic Rift Valley Fever Outbreaks in Humans and Animals in Uganda, October 2017–January 2018. Journal of Environmental and Public Health, 2021, pp.1–8. doi:https://doi.org/10.1155/2021/8881191.
• Bullen, M., Heriot, G.S. and Jamrozik, E. (2023). Herd immunity, vaccination and moral obligation. Journal of Medical Ethics, [online] 49(9). doi:https://doi.org/10.1136/jme-2022-108485.
• Chemison, A., Ramstein, G., Jones, A., Morse, A. and Caminade, C. (2024). Ability of a dynamical climate sensitive disease model to reproduce historical Rift Valley Fever outbreaks over Africa. Scientific reports, 14(1). doi:https://doi.org/10.1038/s41598-024-53774-x.
• Delamater, P.L., Street, E.J., Leslie, T.F., Yang, Y.T. and Jacobsen, K.H. (2019). Complexity of the Basic Reproduction Number (R0). Emerging Infectious Diseases, [online] 25(1), pp.1–4. doi:https://doi.org/10.3201/eid2501.171901.
• Dhungana, H.N. and Ghimire, S. (2021). Commentary: Statistical Modeling for the Prediction of Infectious Disease Dissemination With Special Reference to COVID-19 Spread. Frontiers in Public Health, 9. doi:https://doi.org/10.3389/fpubh.2021.735857.
• Foley, A., Brinklow, L., Corbett, J., Kelman, I., Klöck, C., Moncada, S., Mycoo, M., Nunn, P.D., Pugh, J., Robinson, S., Tandrayen‐Ragoobur, V. and Walshe, R. (2023). Understanding ‘Islandness’. Annals of the American Association of Geographers, 113(8), pp.1800–1817. doi:https://doi.org/10.1080/24694452.2023.2193249.
• Itescu, Y., Foufopoulos, J., Pafilis, P. and Meiri, S. (2019). The diverse nature of island isolation and its effect on land bridge insular faunas. Global Ecology and Biogeography, 29(2), pp.262–280. doi:https://doi.org/10.1111/geb.13024.
• Knopoff, D., Cusimano, N., Stollenwerk, N. and Aguiar, M. (2022). Spatially Extended SHAR Epidemiological Framework of Infectious Disease Transmission. Computational and Mathematical Methods, 2022, pp.1–14. doi:https://doi.org/10.1155/2022/3304532.
• Kovats, S., Hales, S., Campbell-Lendrum, D., Rocklov, J., Honda, Y. and Lloyd, S. (2018). Global Risk Assessment Of The Effect Of Climate Change On Selected Causes Of Death In 2030s And 2050s. ISEE Conference Abstracts, 2015(1), p.1204. doi:https://doi.org/10.1289/isee.2015.2015-1204.
• Mummah, R.O., Hoff, N.A., Rimoin, A.W. and Lloyd-Smith, J.O. (2020). Controlling emerging zoonoses at the animal-human interface. One Health Outlook, 2(1). doi:https://doi.org/10.1186/s42522-020-00024-5.
• Ramirez, V. (2020). What Is R0? Gauging Contagious Infections. [online] Healthline. Available at: https://www.healthline.com/health/r-naught-reproduction-number [Accessed 24 Mar. 2025].
• Tajudeen, Y.A., Oladunjoye, I.O., Bajinka, O. and Oladipo, H.J. (2022). Zoonotic Spillover in an Era of Rapid Deforestation of Tropical Areas and Unprecedented Wildlife Trafficking: Into the Wild. Challenges, 13(2), p.41. doi:https://doi.org/10.3390/challe13020041.
• Tinto, B., Quellec, J., Cetre-Sossah, C., Dicko, A., Salinas, S. and Simonin, Y. (2023). Rift Valley fever in West Africa: A zoonotic disease with multiple socio-economic consequences. One Health, 17, pp.100583–100583. doi:https://doi.org/10.1016/j.onehlt.2023.100583.